{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.layers import *\n",
    "from keras.models import Model, Sequential\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from keras.utils import np_utils\n",
    "import keras\n",
    "import keras.backend as K\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "import sklearn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "from tqdm import tqdm\n",
    "from collections import deque\n",
    "import seaborn as sns\n",
    "import random,time\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_actions = len(set(y_test))\n",
    "image_w, image_h = X_train.shape[1:]\n",
    "\n",
    "X_train = X_train.reshape(*X_train.shape,1)\n",
    "X_test = X_test.reshape(*X_test.shape, 1)\n",
    "\n",
    "#normalization\n",
    "X_train = X_train/255.\n",
    "X_test = X_test/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummy_actions = np.ones((1, num_actions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_onehot = keras.utils.to_categorical(y_train, num_actions)\n",
    "\n",
    "y_test_onehot = keras.utils.to_categorical(y_test, num_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fddb2b9b3c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AUIQfCIrwA0H9H4BpmwJXvvG+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddb5d06240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0].reshape(28,28),'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MnEnviroment(object):\n",
    "    def __init__(self, x,y):\n",
    "        self.train_X = x\n",
    "        self.train_Y = y\n",
    "        self.current_index = self._sample_index()\n",
    "        self.action_space = len(set(y)) - 1\n",
    "    def reset(self):\n",
    "        obs, _ = self.step(-1)\n",
    "        return obs\n",
    "    '''\n",
    "    action: 0-9 categori, -1 : start and no reward\n",
    "    return: next_state(image), reward\n",
    "    '''\n",
    "    def step(self, action):\n",
    "        if action==-1:\n",
    "            _c_index = self.current_index\n",
    "            self.current_index = self._sample_index()\n",
    "            return (self.train_X[_c_index], 0)\n",
    "        r = self.reward(action)\n",
    "        self.current_index = self._sample_index()\n",
    "        return self.train_X[self.current_index], r\n",
    "    \n",
    "    def reward(self, action):\n",
    "        c = self.train_Y[self.current_index]\n",
    "        #print(c)\n",
    "        return 1 if c==action else -1\n",
    "        \n",
    "    def sample_actions(self):\n",
    "        return random.randint(0, self.action_space)\n",
    "    \n",
    "    def _sample_index(self):\n",
    "        return random.randint(0, len(self.train_Y)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env = MnEnviroment(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memory = deque(maxlen=512)\n",
    "replay_size = 64\n",
    "epoches = 2000\n",
    "pre_train_num = 256\n",
    "gamma = 0.  #every state is i.i.d\n",
    "alpha = 0.5\n",
    "forward = 512\n",
    "epislon_total = 2018 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def createDQN(input_width, input_height, actions_num):\n",
    "    img_input = Input(shape=(input_width, input_height,1),dtype='float32',name='image_inputs')\n",
    "    #conv1\n",
    "    conv1 = Conv2D(32,3,padding='same',activation='relu',kernel_initializer='he_normal')(img_input)\n",
    "    conv2 = Conv2D(64,3,strides=2,padding='same', activation='relu',kernel_initializer='he_normal')(conv1)\n",
    "    conv3 = Conv2D(64,3,strides=2,padding='same', activation='relu',kernel_initializer='he_normal')(conv2)\n",
    "    conv4 = Conv2D(128,3,strides=2,padding='same', activation='relu',kernel_initializer='he_normal')(conv3)\n",
    "    x = Flatten()(conv4)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    outputs_q = Dense(actions_num, name='q_outputs')(x)\n",
    "    #one hot input\n",
    "    actions_input = Input((actions_num,),name='actions_input')\n",
    "    q_value= multiply([actions_input, outputs_q])\n",
    "    q_value = Lambda(lambda l:K.sum(l, axis=1,keepdims=True),name='q_value')(q_value)\n",
    "    \n",
    "    model = Model(inputs=[img_input, actions_input], outputs=q_value)\n",
    "    model.compile(loss='mse',optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actor_model = createDQN(image_w,image_h,num_actions) #用于决策\n",
    "critic_model = createDQN(image_w,image_h,num_actions) #用于训练\n",
    "actor_q_model = Model(inputs=actor_model.input, outputs=actor_model.get_layer('q_outputs').output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def copy_critic_to_actor():\n",
    "    critic_weights = critic_model.get_weights()\n",
    "    actor_wegiths  = actor_model.get_weights()\n",
    "    for i in range(len(critic_weights)):\n",
    "        actor_wegiths[i] = critic_weights[i]\n",
    "    actor_model.set_weights(actor_wegiths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_q_values(model_,state):\n",
    "    inputs_ = [state.reshape(1,*state.shape),dummy_actions]\n",
    "    qvalues = model_.predict(inputs_)\n",
    "    return qvalues[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(model,states):\n",
    "    inputs_ = [states, np.ones(shape=(len(states),num_actions))]\n",
    "    qvalues = model.predict(inputs_)\n",
    "    return np.argmax(qvalues,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def epsilon_calc(step, ep_min=0.01,ep_max=1,ep_decay=0.0001,esp_total = 1000):\n",
    "    return max(ep_min, ep_max -(ep_max - ep_min)*step/esp_total )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def epsilon_greedy(env, state, step, ep_min=0.01, ep_decay=0.0001,ep_total=1000):\n",
    "    epsilon = epsilon_calc(step, ep_min, 1, ep_decay,ep_total)\n",
    "    if np.random.rand()<epsilon:\n",
    "        return env.sample_actions(),0\n",
    "    qvalues = get_q_values(actor_q_model, state)\n",
    "    return np.argmax(qvalues), np.max(qvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pre_remember(pre_go = 30):\n",
    "    state = env.reset()\n",
    "    for i in range(pre_go):\n",
    "        rd_action = env.sample_actions()\n",
    "        next_state, reward = env.step(rd_action)\n",
    "        remember(state,rd_action,0,reward,next_state)\n",
    "        state = next_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def remember(state,action,action_q,reward,next_state):\n",
    "    memory.append([state,action,action_q,reward,next_state])\n",
    "    \n",
    "def sample_ram(sample_num):\n",
    "    return np.array(random.sample(memory,sample_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Q(s,a;\\theta_{critic})=(1-\\alpha)Q(s,a;\\theta_{actor})+\\alpha[R^{a}_{s}+\\gamma \\max_{a'}Q(s',a';\\theta_{actor})] $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def replay():\n",
    "    if len(memory) < replay_size:\n",
    "        return \n",
    "    #从记忆中i.i.d采样\n",
    "    samples = sample_ram(replay_size)\n",
    "    #展开所有样本的相关数据\n",
    "    #这里next_states没用 因为和上一个state无关。\n",
    "    states, actions, old_q, rewards, next_states = zip(*samples)\n",
    "    states, actions, old_q, rewards = np.array(states),np.array(actions).reshape(-1,1),\\\n",
    "                                    np.array(old_q).reshape(-1,1),np.array(rewards).reshape(-1,1)\n",
    "   \n",
    "    actions_one_hot = np_utils.to_categorical(actions,num_actions)\n",
    "    #print(states.shape,actions.shape,old_q.shape,rewards.shape,actions_one_hot.shape)\n",
    "    #从actor获取下一个状态的q估计值 这里也没用 因为gamma=0 也就是不对bellman方程展开\n",
    "    #inputs_ = [next_states,np.ones((replay_size,num_actions))]\n",
    "    #qvalues = actor_q_model.predict(inputs_)\n",
    "    \n",
    "    #q = np.max(qvalues,axis=1,keepdims=True)\n",
    "    q = 0\n",
    "    q_estimate = (1-alpha)*old_q +  alpha *(rewards.reshape(-1,1) + gamma * q)\n",
    "    history = critic_model.fit([states,actions_one_hot],q_estimate,epochs=1,verbose=0)\n",
    "    return np.mean(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"{:2f}\".format(2.12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "memory.clear()\n",
    "total_rewards = 0\n",
    "reward_rec = []\n",
    "pre_remember(pre_train_num)\n",
    "every_copy_step = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 512, 2018, 256)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epoches, forward, epislon_total,pre_train_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "pbar = tqdm(range(1,epoches+1))\n",
    "state = env.reset()\n",
    "for epoch in pbar:\n",
    "    total_rewards = 0\n",
    "    epo_start = time.time()\n",
    "    for step in range(forward):\n",
    "        #对每个状态使用epsilon_greedy选择\n",
    "        action, q = epsilon_greedy(env, state, epoch, ep_min=0.01, ep_total=epislon_total)\n",
    "        eps = epsilon_calc(epoch,esp_total=epislon_total)\n",
    "        #play \n",
    "        next_state,reward = env.step(action)\n",
    "        #加入到经验记忆中\n",
    "        remember(state, action, q, reward, next_state)\n",
    "        #从记忆中采样回放，保证iid。实际上这个任务中这一步不是必须的。\n",
    "        loss = replay()\n",
    "        total_rewards += reward\n",
    "        state = next_state\n",
    "        if step % every_copy_step==0:\n",
    "            copy_critic_to_actor()\n",
    "    reward_rec.append(total_rewards)\n",
    "    pbar.set_description('R:{} L:{:.4f} T:{} P:{:.3f}'.format(total_rewards,loss,int(time.time()-epo_start),eps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "critic_model.save('crtic_2000.HDF5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r5 = np.mean([reward_rec[i:i+10] for i in range(0,len(reward_rec),10)],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdad96096a0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R88+PfSCFhTB+PGy2GTRunHZUIiKlUxJJWc+e0LAhtG4NI0fCNdfE6qvmzdOO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\nERERKTclERERKbf/AwheI/5hFilUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdad95f80b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(r5)),r5,c='b')\n",
    "plt.xlabel('iters')\n",
    "plt.ylabel('mean score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "copy_critic_to_actor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_loaded = keras.models.load_model('crtic_2000.HDF5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97230000000000005"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = predict(actor_q_model, X_test)\n",
    "\n",
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vV3UWvVOfPn1kZ4gAAOhu6McCAAAAUDzUWAAAAACKhxoLAAAAQPFQYwEAAAAo\n3mvGvJ88eVL5ecDbjF3r1ys/eNevX6deumu9HnvvAAA67b/mef/111+nT5+u2JvdAgC8oUxNTdlt\ntgEAOkGAO0VAT4N7mAAAQC+A8VgAAAAAiocaCwAAAEDxUGMBAAAAKB5qLAAAAADFQ40FAAAAoHio\nsQAAAAAUDzUWAAAAgOKhxgIAAABQPNRYAAAAAIqHGgsAAABA8VBjAQAAACgeaiwAAAAAxUONBQAA\nAKB4qLEAAAAAFA81FgAAAIDiocYCAAAAUDzUWAAAAACKhxoLAAAAQPFQYwEAAAAoHmosAAAAAMVD\njQUAAACgeKixAAAAABQPNRYAAACA4qHGAgAAAFA81FgAAAAAiocaCwAAAEDxUGMBAAAAKB5qLAAA\nAADFQ40FAAAAoHiosQAAAAAUDzUWAAAAgOKhxgIAAABQPNRYAAAAAIqnoeoEAIiIoqOjnz59yh7/\n5z//IaLdu3fzz06bNs3Ozk41mQEAAHSKgOM4VecAQH/605/Ky8v79OnT8qmXL196e3tHREQoPysA\nAIBOw7lC6BEWL16srq7+8nWIyNXVVdUJAgAAdAz6saBHuHr1qoODw2uf+tOf/lRUVKSmhv8HAADg\nTYLfLegR7O3tTU1NW7Zramr+5S9/QYEFAABvHPx0QY8gEAjc3d1bjscSi8VLly5VSUoAAABdgXOF\n0FP85z//sbW1bdZobm7+5MkTleQDAADQFejHgp7ivffeGzVqlGyLpqamh4eHqvIBAADoCtRY0IOs\nWLFC9nShWCxesmSJCvMBAADoNJwrhB4kKyvr3XffZZ9JgUDw3nvv/f7776pOCgAAoDPQjwU9iLm5\n+bhx4wQCARGpq6vjRCEAALy5UGNBz/Lpp5+qq6sTkUQicXNzU3U6AAAAnYRzhdCzFBUVDR06lOO4\njz766Ndff1V1OgAAAJ2EfizoWYyNjadMmcJxHE4UAgDAG00x/Vj+/v579+7tehwAAIXT0NBITU2d\nNGmSqhMBgLeLhkKiFBQU/PnPf/b391dINOjJWDHt5+fXfZvgOK6iomLQoEHdt4nXSk9P37t3b3x8\nvJK3C93Nzc3t2bNnqs4CAN46iqmxiMjMzMzV1VVR0aDHOnnyJBH1yvea9en2yl0DAADlw3gsAAAA\nAMVDjQUAAACgeKixAAAAABQPNRYAAACA4qHGAgAAAFA81FgAAAAAiocaC9BrHrgAACAASURBVJRh\nwoQJQqFQ1Vl0r8zMzNDQ0Li4OFtbW4FAYGNjU1dXxz+bkpIye/ZsgUAwfvz4uLg4JefGcdyhQ4fG\njh2ro6Nja2sbFRXFTz7McdyxY8ecnZ03bNgwbdq0devWVVRUqComERUWFkZFRbm5uU2cOJFvbGpq\nWr9+fUFBQUd2GgBA1ThFcHV1dXV1VUgo6OE6914vXrx406ZN3ZEPk5eX1/UgrPTp3GsvX768dOnS\nly9fchxXWVnJ/ri8vLxk18nOziaiR48edT3VjgoMDFy+fLlIJPLx8enXrx8RhYeHs6e++eYbIkpO\nTuY47t69e0Tk4uKiqphMbm4uEY0aNUq2sbS09JNPPnny5In8cXhEFBcX14kXAgB0BWos6Jge+F5n\nZWU5ODh0PU6na6z79++bmZmVlpbyLUTE7txy4sQJvlEsFhMRq8OUKS8vb9myZfzi+fPnicjCwoIt\nsu6ikpISjuOkUqmBgYGOjo5KYspqWWNxHPf777/b2NhUV1d3KBSHGgsAVATnCuHNVlBQMG/evOfP\nn6sqAalU6u7uvnLlysGDB8u2x8XFGRsbr169Oisri7X06dOHiDQ1NZWcYW5ublhYGL84a9YsAwOD\nkpIStsjuWfTzzz8TUW1tbXl5+fTp01USs122trYWFha9/qQzAPQaqLGge0kkkvj4eA8Pj8mTJ3Mc\nd/r06dWrV5uamlZUVHh4eAwePHj06NG3bt3iOC49PT0gIGD48OFFRUULFy4cNGjQ6NGjExISiOjA\ngQMCgUAgEBBRVVVVWFgYv3jkyJEHDx4UFRWtXbuWbTE1NdXU1PTKlSvK2cEzZ87cvXv3448/btZu\nYmISHx9fW1u7ZMkS1oPVTGVlpVAoDAoK8vf3nzVrlr+/f0VFRRuHiL2qvr5+9+7dnp6e48ePnzlz\n5h9//NFuhg4ODsbGxrItYrGYv0Hy3r17zc3NfX19c3NzRSKRUCiMjY1VSUx5zJ49++DBg0+ePFFI\nNACA7qWQ3rAeeP4Iukkn3mt+eI1UKs3Pz9fW1iai7du35+TkREdHE5GdnV1TU1NSUhIb1uPt7X3l\nypWYmBgdHR0iSktL4zjO3Nxc9uMqu0j/fV4pMTFRS0vrzJkzHd21zp0rXLJkCRGJxWLZRj7Onj17\niCggIKBZe1VVlaWl5datW9licXGxpaXliBEjysvLWztEbM1Vq1Y9fPiQPXZ0dDQ0NKysrOxQwmlp\naf369bt9+zbfUlJSYm9vP3ToUD8/vw6F6r6Y9LpzhRzH3blzh4hCQkI6Gg3nCgFA+VBjQcd04r2W\nSqWyP5kjR47kSw2pVGpoaKipqckWLS0tiaimpoYt7t27l4gWL17McdyoUaNkCyDZxZa/x42NjR3e\nsc7WWMOGDdPV1W3WKLuDixYtolcDwPn2jRs3EtHTp0/5lxw9epSIhEIh1/ohun79est/k5KSkuTP\ntrGxcfLkybGxsbKNOTk5c+fOZV1xX3zxhUQi6dAR6I6YrdVYhYWFROTk5NTRaKixAED5cK4Quh07\nqffaRYFAoK+vz59KU1NTIyLWi0NE8+fPJ6LHjx93dIsaGhqdzrajioqK9PX1W3tWIBBERUVZWVl5\neHg8ffqUb7969SoRDRgwgG+ZPHkyEV27do1aP0Q3b960trZu9jc8b948+bPdtm3bjBkzli5dyrfc\nuHHjgw8+8PDwSExMtLe3Dw0N3bJli/wBuylma/T09IiouLhYIdEAALoVaizouYYMGUJEZmZmqk6k\nLerq6hKJpI0VBgwYkJCQUF9fv3z5cr6RVZM5OTl8i5GRERHp6uq2EaqsrCw7O7u2tla2se2ty0pK\nStLW1m5W7mzYsKGsrGzq1Kl9+/Y9ceIEEX377bdyBuymmG1g1Sf3aiIuAICeDDUW9FxlZWVENHPm\nTHr14/ry5UsikkqlbA4q/re2qalJ9oXNFruViYnJixcvZFtY0SNb+lhbW0dFRbHr7BjWa5WcnMy3\n5Ofn06udbY2VlRUb8863PHjwQCQSyZPnxYsXCwsLg4KC+BbWZ8Z6yNjVjmZmZoaGhs36HZUcs21s\nLlMTExOFRAMA6FaosaDbVVdXE1FVVRVbbGhoIJnyiD3b2NjIr89XSJcuXRo3btyaNWuIyMrKioi2\nb9/++PHj/fv3s2LrwoULEonEwsLi2bNneXl57FVnz57V09P78ccflbJzNGXKlOrqarYXDJvCoNn5\nLDc3N19fX35x/fr1NjY2ERERz549Yy2RkZH29vbe3t7U+iFycXEZMWJEcHCwp6dnTEzMpk2bfH19\nV65cSUShoaHW1tbHjx9/bZIpKSm7du1qamoSiUQikSgiIsLPz+/cuXNEtGzZMiJij3Nzc0tKStgo\nfpXEZNj8+K/tnystLSUiBweHNl4OANBTKGRUF8a8vz06+l7X1NTw/RxhYWEhISHscXBw8IsXL9io\ndiIKDAysq6tjI9m/+uqr58+fFxcX79y5k59wMiMjw87Orn///o6OjhkZGQ4ODu7u7sePH29oaAgK\nCjI2Nv7hhx/YmhcvXjQxMbl06VJHd61zY95Z79SFCxfYYkJCgpOTExHNnTv3l19+kV1TLBZ/9NFH\n/GJVVZVQKHR0dPT39xcKhV9++WVDQwPHcXy/1GsPUXZ2trOzs76+vpGRkZeXF5vnk+O4devWCQSC\nIUOGtMzw6tWrWlpaLf/22ZzpUqlUJBJ9+OGH/v7+CxYs2Lx5c319vUpiMqmpqV5eXkSkoaGxe/fu\nu3fvyj779ddfq6mpdXS2d8KYdwBQBQGniJENbm5uRBQfH9/1UNDDdet7bWVllZGRoZDPZCfEx8ez\naxg7+sI5c+aMHDly37593ZGV/DIzM1esWHHjxo1eHNPZ2dnY2PjgwYMdepVAIIiLi2MfXQAApcG5\nQoCuOnz4cHJyclFRkQpzqK2tDQ8PP3ToUC+OmZ6enpmZKTu/PABAT4Yai5oNWAYVYlfM1dTUqDqR\njjEyMkpISPDz82t2xZ8yZWVlhYSEjBkzprfGLCws3LFjR0pKysCBAxWYDwBA9+mdNRbHcYcOHRo7\ndqyOjo6trW1UVFTLE0ANDQ07duyYOHFis9vMvVZFRcW6deu2bNni6+vbbKIjeZKJj4+fN2/e+++/\nP2vWrPnz53/22We7du0KCAjo2F51ZIuv3f2ffvrJycmJ3YVm2rRp06ZNGz9+/Pz58w8dOsSGkKtQ\nTU3Nxo0bCwoKiMjHxyc9PV21+XTUe++9t2PHjsjISFUlMGbMGIUXHz0nZmNjY3R0dGxsbA+fyAMA\n4L8oZFRXTxvzHhgYuHz5cpFI5OPjw27PEh4e3nK1uro6Nntk29Hq6upGjhy5Y8cOtnjw4EEjI6OC\nggJ5MikpKZk6daqFhcX169elUinHcRKJJDo6etCgQX/96187uFvyamP3WREzfPhwtiiRSE6fPm1u\nbv7uu+/eu3dPnuA97b1WoM6NeYeejzDmHQBUoRf2Y+Xn5+fn53///fefffbZ/v37ExMTiWj//v0t\n19TS0jI0NGw3YHh4eGZmJrsjChF5eHiIxeKtW7e2+0KpVOri4vLvf//7xo0bEyZMYFMEqampubu7\nJyQkdNN5pbZ3f+jQoUTUt29ftqimpjZ//vy0tLSamhoXF5f6+vruSAkAAOAt1AtrrNzcXNlRsbNm\nzTIwMGBTFnXOlStXiOidd95hi3369Pnggw9OnjzJtXcB2qlTp9LT04OCglqejpw6daqrq2unU2pD\nJ3bfxMQkODj4yZMnGE0MAACgKEqtsWpqaoKDg93d3X18fKZMmbJv3z5WplRWVgqFwqCgIH9//1mz\nZvn7+1dUVHAcd/r06dWrV5uamlZUVHh4eAwePHj06NG3bt0iopMnTw4aNEggEGzatIkFZxPnfPvt\ntw4ODsbGxrLbFYvFkyZNYo/r6ur8/f1Xr169adOmDRs2yNOZxCaTLC8v51sMDAyqqqrYdWSpqamm\npqasDmvm1KlTRDRjxozXhl24cCF7oMzdb82iRYvU1NQuXrzY7tEAAAAAuSjkjKM8Y3TEYvGUKVPc\n3d0lEgnHcVFRUUR05syZqqoqS0vLrVu3stWKi4stLS1HjBhRXl6en5/Pbg+8ffv2nJyc6OhoIrKz\ns2NrhoeHE9G5c+fYYm5u7tKlS1tuNy0trV+/frdv3+Y4rrGx0c7ObtWqVWxo1P/93/+pq6u3exDY\nzW6PHj3Kt6xYsYKI8vLyOI5LTEzU0tI6c+ZMyxeOHz+eiF68eNFGcGXuPkNEo0aNarmmsbHxoEGD\n2jwSHIfxWPAGIozHAgBVUN4cpHv27AkICHj06BGby7upqSk6OnrBggWhoaEhISFPnz7l70F27Ngx\nDw8PoVD4z3/+c9SoUZmZmSxJjuOMjY1fvHjBLoITi8Xvvvvu2LFjz5w5Q0SbN2/+3//93/fff192\no01NTTNmzFi7di2rk0Qi0eeff37//n1ra2u2wsiRIx8/ftz2Qfjtt9/+/Oc/Gxsbnzx5cvTo0Skp\nKd7e3s+fP29oaNDQ0GBbYQ+a+fOf/3zjxg3ZXWvpH//4h9J2nxEIBKNGjXr06FGzTMzMzCQSSbuX\nTLq5uRUUFPj5+bW92psoPT197969mEq393Fzc8McpACgfK+pDLoJu+WIqanp/9+whga7z9rVq1eJ\naMCAAfya7Ha57OaysreSFQgE+vr6/NAiTU3Nv//970Kh8MmTJ2ZmZhkZGc0qDCLatm3bjBkz+AqD\nnQsbMWIEv4KaWvtnS+3s7JKTkzdt2jR79ux33303ICBAKpVOmzaNr6teW2ARkbW19Y0bNx4+fNhG\njaXM3W+DWCwuLi5u+4bEvPT09DdubgX54ZcYAAAUQnnjsdiopsePHzfPQE2NiHJycvgWIyMjItLV\n1W035qpVq7S1tUUiUWJiIn/dHy8pKUlbW3vLli18S2FhIRGVlZV1NHknJ6fbt2/X1NT8/vvvurq6\nJSUlf/nLX9p91ZQpU4jo+vXrbayjzN1vQ2pqamNjY2tDx5rBuUJ4s8jzqQYAUDjl1Vi2trZEtGPH\nDqlUylpycnLOnTvHum2Sk5P5NfPz84lInj4VXV3dVatWRUVFxcXFffLJJ7JPXbx4sbCwkL8bMRFd\nu3bNysqq2bY6qqamRigUTpo0SbZzqKmp6bUru7u7jxs3bv/+/S1PwDU0NBw5coRe9VopZ/dbi/Py\n5cuNGzeOHTvWx8en3Y0CAACAPJRXYwUFBfXv3/+HH36YOXNmZGTk5s2bd+7c+fHHH69fv97GxiYi\nIuLZs2dszcjISHt7e29vbyJqaGggIv4/0erqaiJqbGzkw/r4+NTU1Lz//vt9+vThG1NSUnbt2tXU\n1CQSiUQiUUREhJ+f37lz54RCobq6+saNG8+fP19fX5+amsqqn+zsbHl2QSwWe3p6ElFsbCx/kvHs\n2bN6eno//vhjy/XV1dW///77fv36OTg4nDp1ipVidXV1qampc+fOZQWfMnefbZ0Py9y5c8fR0bGi\noiImJkY2CAAAAHSF8sZjmZubX79+/Ysvvvjtt98yMjJcXV3/+c9/qqmp9e/fPz09PTg42MPDY8yY\nMerq6gYGBqmpqX369ImMjMzNzSWiHTt2fP7554cPH2Yl0ebNm7du3aqlpUVEI0aM+Pzzz9etW8dv\n6Nq1a/Pnz6+vr798+bJsAk+ePDE3N09NTd2wYYOrq6uhoaGXl9fYsWOtra2zsrLeeecddo1ha+7f\nv79y5UpLS8tffvmFnc5j+vbtO3DgQH5Wz2b+53/+5969e19//fV3330XEBCgra2toaExd+7c+Ph4\nNmmWMnc/LS3t8OHDRJSbmzt16tS+ffv27du3T58+ixcv9vDw0NHRkeuNBAAAADko77rCN1dOTs7R\no0fV1dWdnZ3ZGc+3WS9+r+Pj4xcvXozhO72PQCDAdYUAoHzK68fq4WSv4Gvm4cOH8tw5BwAAAICH\nGuv/Q+8FAAAAKFAvvF8hAAAAgMqhxgLopMzMzNDQ0Li4OFtbW4FAYGNjwy7bZFJSUmbPni0QCMaP\nH89m3lImjuMOHTo0duxYHR0dW1vbqKgovqeW47hjx445Oztv2LBh2rRp69atq6ioUFVMIrp//76L\ni8vgwYMNDAyWLFnCT3TS2uaamprWr19fUFDQsSMCAKB8Cpnirxffww6a6e73mt0FUiVBOjQH6eXL\nl5cuXfry5UuO4yorK9lfk5eXl+w6bE6QR48edSKZLgoMDFy+fLlIJPLx8enXrx8RhYeHs6e++eYb\nIkpOTuY47t69e0Tk4uKiqpj3799fsGDBqVOn7ty54+7uTkTTp09vd3OlpaWffPLJkydP5DwahPsV\nAoAqoMaCjunW9zorK8vBwUFVQeSvse7fv29mZlZaWsq3ENGkSZOI6MSJE3yjWCwmIlaHKVNeXt6y\nZcv4xfPnzxORhYUFW5w4cSIRlZSUcBwnlUoNDAx0dHRUEpPjuH379tXW1rLHYrFYV1dXW1u73c1x\nHPf777/b2NhUV1fLsxXUWACgEjhXCD1FQUHBvHnznj9/rvIgbZNKpe7u7itXrmQznPHi4uKMjY1X\nr16dlZXFWticrpqamt2XzGvl5uaGhYXxi7NmzTIwMODvdDlo0CB6df/Q2tra8vLy6dOnqyQmEf39\n73/v378/v9jU1MSm+W17c0Rka2trYWEhFArl2QoAgEqgxoJuUVlZKRQKg4KC/P39Z82a5e/vzwbo\nHDhwQCAQsJkyqqqqwsLC+MUjR448ePCgqKho7dq1HMelp6cHBAQMHz68qKho4cKFgwYNGj16dEJC\ngvxBWCapqammpqZXrlxR1K6dOXPm7t27H3/8cbN2ExOT+Pj42traJUuWsB4seY4Jx3GnT59evXq1\nqalpRUWFh4fH4MGDR48efevWLfaq+vr63bt3e3p6jh8/fubMmX/88Ue7GTo4OBgbG8u2iMVi1s1G\nRHv37jU3N/f19c3NzRWJREKhMDY2ViUxZUml0i1btuzbt2/fvn3tbo6ZPXv2wYMHnzx50qENAQAo\nj0J6w3Cu8O0hz3tdVVVlaWm5detWtlhcXGxpaTlixAhWUpibm8t+8GQXiWjUqFEcxzU1NSUlJbFR\nON7e3leuXImJiWEz0aelpckZhElMTNTS0jpz5ky7uybnucIlS5YQkVgslm3kX7hnzx4iCggIaNbe\n2jEpLy/Pz8/X1tYmou3bt+fk5ERHRxORnZ0dW3PVqlUPHz5kjx0dHQ0NDSsrK9tNUlZaWlq/fv1u\n377Nt5SUlNjb2w8dOtTPz69Dobop5qlTp1j9NHz48IMHD0ql0nY3x3HcnTt3iCgkJKTd+IRzhQCg\nCqixoGPkea83btxIRE+fPuVbjh49SkRCoZDjuFGjRsmWMrKLzcojS0tLIqqpqWGLe/fuJSI2Fbv8\nQTiOa2xslGfX5Kyxhg0bpqur26yRf6FUKl20aBG9GgDOt7d9TEaOHCkbwdDQUFNTk+O469evt/y/\nKCkpSZ7dYRobGydPnhwbGyvbmJOTM3fuXNYV98UXX0gkEvkDdkfM8vLy+/fvR0REsDtEHT58uN3N\ncRxXWFhIRE5OTu3GR40FACqBc4WgeFevXiWiAQMG8C2TJ08momvXrnUoDrvxNuvjIaL58+cT0ePH\njzuaj4aGIufaLSoq0tfXb+1ZgUAQFRVlZWXl4eHBT0NA7R0T2dsMCAQCfX19drbx5s2b1tbWzf5o\n582bJ3+227ZtmzFjxtKlS/mWGzdufPDBBx4eHomJifb29qGhoVu2bJE/YHfE1NfXt7a29vb2PnDg\nABEdO3as7c0xenp6RFRcXNyh5AEAlAY1Figeq41ycnL4FnYXbV1d3a6EHTJkCBGZmZl1KbkuU1dX\nl0gkbawwYMCAhISE+vr65cuX842dOyZlZWXZ2dm1tbWyjW1vXVZSUpK2tnazcmfDhg1lZWXspuAn\nTpwgom+//VbOgN0Uk+fi4kL/fZXAazfHsMKUwx0aAKCnQo0Fisd6aJKTk/mW/Px8Ipo5cya9+ml8\n+fIlEUmlUja5FP9L2dTU1FrYsrKyzgVpI2YnmJiYvHjxQraFFT2ypY+1tXVUVBS7zo5p+5i0xsrK\nio1551sePHggEonkyfPixYuFhYVBQUF8C+szYz1krI4xMzMzNDRs42adSogp69mzZ0Q0Z86ctjfH\nsKsoTExMOrEhAAAlQI0Fird+/XobG5uIiAj2k0lEkZGR9vb23t7eRGRlZUVE27dvf/z48f79+1md\ndOHCBYlEYmFh8ezZs7y8PNlofIV06dKlcePGrVmzpkNBzp49q6en9+OPPypq76ZMmVJdXV1dXc23\nsDkFmp20cnNz8/X1lfOYNDQ0kEyNyII3Nja6uLiMGDEiODjY09MzJiZm06ZNvr6+K1euJKLQ0FBr\na+vjx4+/NsmUlJRdu3Y1NTWJRCKRSBQREeHn53fu3DkiWrZsGRGxx7m5uSUlJWwUv0pi7tmz57vv\nvmM1a0NDQ2BgoJubGzsmbWyOKS0tJSIHB4fXRgYAUD2FjOrCmPe3h5zvdVVVlVAodHR09Pf3FwqF\nX375ZUNDA3sqIyPDzs6uf//+jo6OGRkZDg4O7u7ux48fb2hoCAoKMjY2/uGHH9iabCT7V1999fz5\n8+Li4p07d/JzTsof5OLFiyYmJpcuXWo3ZznHvLPeqQsXLrDFhIQEJycnIpo7d+4vv/wiu6ZYLP7o\no4/aPSZ8v1RwcPCLFy/Y0H4iCgwMrKury87OdnZ21tfXNzIy8vLyYvN8chy3bt06gUAwZMiQlhle\nvXqVDR5vhk2MLpVKRSLRhx9+6O/vv2DBgs2bN9fX16skJsdxW7dutbCw0NPTW7t2rY+Pz08//cQu\nKmx7c8zXX3+tpqYmz2zvhDHvAKAKAk4Roxnc3NyIKD4+vuuhoIdT5nttZWWVkZGhkI+oPOLj49lF\ni+2uOWfOnJEjR7KZnFQoMzNzxYoVN27ceAtjEpGzs7OxsfHBgwfbXVMgEMTFxbGPLgCA0uBcIUCH\nHT58ODk5uaioSIU51NbWhoeHHzp06C2MSUTp6emZmZmyc8EDAPQ0qLGg52LX09XU1Kg6keaMjIwS\nEhL8/PyaXfGnTFlZWSEhIWPGjHkLYxYWFu7YsSMlJWXgwIEKDAsAoFiosaAnqqmp2bhxY0FBARH5\n+Pikp6erOqPm3nvvvR07dkRGRqoqgTFjxii8wngjYjY2NkZHR8fGxqp8Fg8AgLYpcm5GAEXR0dEJ\nCQkJCQlRdSJtMTc3X79+vaqzeOv06dNHdjYHAIAeC/1YAAAAAIqHGgsAAABA8VBjAQAAACgeaiwA\nAAAAxVPYmPf09HRM8fc2YJf49cr3mt1AsFfuGgAAKJ9i5nk/efLkyZMnux4HgIiKi4v/+OOPtm+W\nDCA/dXX1nTt3Dh8+XNWJAMDbRTE1FoACyX9PGwAAgB4L47EAAAAAFA81FgAAAIDiocYCAAAAUDzU\nWAAAAACKhxoLAAAAQPFQYwEAAAAoHmosAAAAAMVDjQUAAACgeKixAAAAABQPNRYAAACA4qHGAgAA\nAFA81FgAAAAAiocaCwAAAEDxUGMBAAAAKB5qLAAAAADFQ40FAAAAoHiosQAAAAAUDzUWAAAAgOKh\nxgIAAABQPNRYAAAAAIqHGgsAAABA8VBjAQAAACgeaiwAAAAAxUONBQAAAKB4qLEAAAAAFA81FgAA\nAIDiocYCAAAAUDzUWAAAAACKhxoLAAAAQPFQYwEAAAAoHmosAAAAAMVDjQUAAACgeKixAAAAABRP\nwHGcqnMAIGdn55ycHPa4rq6urKzMzMyMf3b16tWff/65ajIDAADoFA1VJwBARJSdnX3//n3ZlsrK\nSv5xdXW10jMCAADoEpwrhB7h008/1dBoteJ3c3NTZjIAAABdh3OF0CPk5+cPGzas5adRIBB88MEH\nN2/eVElWAAAAnYZ+LOgRzMzMJkyYoKbW/AOprq7+6aefqiQlAACArkCNBT3FihUrBAJBs0apVIoT\nhQAA8CZCjQU9RctaSl1dferUqUZGRirJBwAAoCtQY0FPYWBgMGPGDHV1ddnGFStWqCofAACArkCN\nBT2Iu7u77LB3NTW1BQsWqDAfAACATkONBT3IggUL+vTpwx5raGjMnTtXT09PtSkBAAB0Dmos6EEG\nDBjg7OzMyiyJROLu7q7qjAAAADoJNRb0LMuXL29qaiIiLS2tOXPmqDodAACATkKNBT2Lk5OTtrY2\nES1atEhLS0vV6QAAAHRSJ+9X2NDQcO7cOYlEothsAIjoww8/vHz5spmZ2cmTJ1WdC/RCxsbGkyZN\nUnUWAND7dfJeOqdOnVq4cKHCswEA6G4aGhqNjY2qzgIAer9O9mOxETO41yEIBIK4uLheORU726n4\n+HhVJwKKFB8fv3jxYlVnAQBvBYzHAgAAAFA81FgAAAAAiocaCwAAAEDxUGMBAAAAKB5qLAAAAADF\nQ40FAAAAoHiosQAAAAAUDzUWqMCECROEQqGqs1CwzMzM0NDQuLg4W1tbgUBgY2NTV1fHP5uSkjJ7\n9myBQDB+/Pi4uDgl58Zx3KFDh8aOHaujo2NraxsVFcVPbsdx3LFjx5ydnTds2DBt2rR169ZVVFSo\nKiYR3b9/38XFZfDgwQYGBkuWLHn69Gnbm2tqalq/fn1BQUHHjggAgBJwncJ+JDr3WuhNiCguLq6j\nr1q8ePGmTZu6Ix8mLy+v60FcXV1dXV3lXPny5ctLly59+fIlx3GVlZXsj8vLy0t2nezsbCJ69OhR\n13PrqMDAwOXLl4tEIh8fn379+hFReHg4e+qbb74houTkZI7j7t27R0QuLi6qinn//v0FCxacOnXq\nzp077u7uRDR9+vR2N1daWvrJJ588efJEnk3guwsAlAY1FnRJ52qsbpWVleXg4ND1OPLXWPfv3zcz\nMystLeVbiIjdEe/EiRN8o1gsJiJWhylTXl7esmXL+MXz588TkYWFT7N9ygAAIABJREFUBVucOHEi\nEZWUlHAcJ5VKDQwMdHR0VBKT47h9+/bV1tayx2KxWFdXV1tbu93NcRz3+++/29jYVFdXt7sJfHcB\ngNLgXCH0KgUFBfPmzXv+/LnStiiVSt3d3VeuXDl48GDZ9ri4OGNj49WrV2dlZbGWPn36EJGmpqbS\ncmNyc3PDwsL4xVmzZhkYGJSUlLDFQYMGEdHPP/9MRLW1teXl5dOnT1dJTCL6+9//3r9/f36xqanJ\n09Oz3c0Rka2trYWFRe87AQ0AbzTUWKBUEokkPj7ew8Nj8uTJHMedPn169erVpqamFRUVHh4egwcP\nHj169K1btziOS09PDwgIGD58eFFR0cKFCwcNGjR69OiEhAQiOnDggEAgEAgERFRVVRUWFsYvHjly\n5MGDB0VFRWvXrmVbTE1NNTU1vXLlSjft0ZkzZ+7evfvxxx83azcxMYmPj6+trV2yZAnrwWqmsrJS\nKBQGBQX5+/vPmjXL39+/oqKijWPCXlVfX797925PT8/x48fPnDnzjz/+aDdDBwcHY2Nj2RaxWMy6\n2Yho79695ubmvr6+ubm5IpFIKBTGxsaqJKYsqVS6ZcuWffv27du3r93NMbNnzz548OCTJ086tCEA\ngG7Uue4v9LcDQx0/V5ibm0tEo0aNkkql+fn52traRLR9+/acnJzo6GgisrOza2pqSkpKYsNuvL29\nr1y5EhMTo6OjQ0RpaWkcx5mbm8t+AmUXWXD+qcTERC0trTNnznR01+Q8V7hkyRIiEovFso18Mnv2\n7CGigICAZu1VVVWWlpZbt25li8XFxZaWliNGjCgvL2/tmLA1V61a9fDhQ/bY0dHR0NCwsrKyQ/uV\nlpbWr1+/27dv8y0lJSX29vZDhw718/PrUKhuinnq1ClWPw0fPvzgwYNSqbTdzXEcd+fOHSIKCQlp\nOzi+uwBAaVBjQZd0osaSSqWyZdDIkSP5z5JUKjU0NNTU1GSLlpaWRFRTU8MW9+7dS0SLFy/mOG7U\nqFGyn0DZxWY1FsdxjY2NHd4xuWusYcOG6erqNmuU3aNFixbRqwHgfPvGjRuJ6OnTp/xLjh49SkRC\noZBr/Zhcv3695b9JSUlJ8u9UY2Pj5MmTY2NjZRtzcnLmzp3LuuK++OILiUQif8DuiFleXn7//v2I\niAgtLS0iOnz4cLub4ziusLCQiJycnNoOju8uAFAanCsEZWMn9V67KBAI9PX1+TNrampqRMQ6dYho\n/vz5RPT48eOOblFDQ6PT2barqKhIX1+/tWcFAkFUVJSVlZWHhwc/DQERXb16lYgGDBjAt0yePJmI\nrl27Rq0fk5s3b1pbWzf7G543b5782W7btm3GjBlLly7lW27cuPHBBx94eHgkJiba29uHhoZu2bJF\n/oDdEVNfX9/a2trb2/vAgQNEdOzYsbY3x+jp6RFRcXFxh5IHAOg+qLHgjTFkyBAiMjMzU3Ui/0Vd\nXV0ikbSxwoABAxISEurr65cvX843svIxJyeHbzEyMiIiXV3dNkKVlZVlZ2fX1tbKNra9dVlJSUna\n2trNyp0NGzaUlZVNnTq1b9++J06cIKJvv/1WzoDdFJPn4uJC/32VwGs3x7DClHs1RxcAgMqhxoI3\nRllZGRHNnDmTXv2gvnz5koikUimbkor/fW1qapJ9YbNFxTIxMXnx4oVsCyt6ZEsfa2vrqKgodp0d\nw3qtkpOT+Zb8/Hx6tXetsbKyYmPe+ZYHDx6IRCJ58rx48WJhYWFQUBDfwvrMWA8Zq2PMzMwMDQ2b\ndTQqOaasZ8+eEdGcOXPa3hzDpjk1MTHpxIYAALoDaixQturqaiKqqqpiiw0NDSRTHrFnGxsb+fX5\nCunSpUvjxo1bs2YNEVlZWRHR9u3bHz9+vH//flZsXbhwQSKRWFhYPHv2LC8vj73q7Nmzenp6P/74\nYzftzpQpU6qrq1naDJtToNlJKzc3N19fX35x/fr1NjY2ERERrIwgosjISHt7e29vb2r9mLi4uIwY\nMSI4ONjT0zMmJmbTpk2+vr4rV64kotDQUGtr6+PHj782yZSUlF27djU1NYlEIpFIFBER4efnd+7c\nOSJatmwZEbHHubm5JSUlbBS/SmLu2bPnu+++YzVrQ0NDYGCgm5sbOyZtbI4pLS0lIgcHh9dGBgBQ\ngc4N48K4UWCog2Pea2pq+H6IsLCwkJAQ9jg4OPjFixdsVDsRBQYG1tXVsZHsX3311fPnz4uLi3fu\n3MlPMpmRkWFnZ9e/f39HR8eMjAwHBwd3d/fjx483NDQEBQUZGxv/8MMPbM2LFy+amJhcunSpo7sm\n55h31jt14cIFtpiQkODk5EREc+fO/eWXX2TXFIvFH330Eb9YVVUlFAodHR39/f2FQuGXX37Z0NDA\ncRzfL/XaY5Kdne3s7Kyvr29kZOTl5cXm+eQ4bt26dQKBYMiQIS0zvHr1Khs83gybGF0qlYpEog8/\n/NDf33/BggWbN2+ur69XSUyO47Zu3WphYaGnp7d27VofH5+ffvqJXVTY9uaYr7/+Wk1Nrd3Z3vHd\nBQBKI+A6NXwhPj6eXd7ViddCbyIQCOLi4tzc3LojuJWVVUZGhqo+Zmyn4uPj211zzpw5I0eOZDM5\nqVBmZuaKFStu3LjxFsYkImdnZ2Nj44MHD7a9Gr67AEBpcK4QoKsOHz6cnJxcVFSkwhxqa2vDw8MP\nHTr0FsYkovT09MzMTNm54AEAVO5trLGajVCGHotdQFdTU6PqRNphZGSUkJDg5+fX7Io/ZcrKygoJ\nCRkzZsxbGLOwsHDHjh0pKSkDBw5UYFgAgC7qJTUWx3GHDh0aO3asjo6Ora1tVFRUy3MBDQ0NO3bs\nmDhxYrP7yrWmsLAwKirKzc2N3eBWdlvfffedq6vrP/7xj1WrVnXoJiEcx8XHx8+bN+/999+fNWvW\n/PnzP/vss127dgUEBMgfpENaOzI//fSTk5MTuwXNtGnTpk2bNn78+Pnz5x86dIiNH1etmpqajRs3\nFhQUEJGPj096erqqM2rHe++9t2PHjsjISFUlMGbMGIVXGG9EzMbGxujo6NjY2J42qQcAQC8Z8x4Y\nGLh8+XKRSOTj48NuwBIeHt5ytbq6OjZdpJxh+bu+yDZu27Zt2LBh5eXlHMeVl5cPGzZs37598kQr\nKSmZOnWqhYXF9evX2UheiUQSHR09aNCgv/71r3Km1FFtHBlWwQwfPpwtSiSS06dPm5ubv/vuu/fu\n3ZMzPnV8nvc3hZxj3uHN0tO+uwCgF+sN/Vj5+fn5+fnff//9Z599tn///sTERCLav39/yzW1tLQM\nDQ3lj/zOO+80a8nNzQ0ODl6zZg2r1fT19b28vDZu3MimbmqDVCp1cXH597//fePGjQkTJrDpgtTU\n1Nzd3RMSErrpHFPbR2bo0KFE1LdvX7aopqY2f/78tLS0mpoaFxeX+vr67kgJAADgLdEbaqzc3FzZ\nsa6zZs0yMDBgcxQpXExMTFNT04wZM/iW6dOn19XVtTuG99SpU+np6UFBQS3PVE6dOtXV1VXxuXbq\nyJiY/D/27jwgiiPvH/9nuFU8OMKheABBEVCUZVk1KhoRNCquq4AiSrxIfEKIQAaRxTVZBC88gexG\nXDQhxIDx4tDVRQyPInhEkzUihyCKIBAUQe6rv3/UL/2bB2QchmEGzPv113R1V/WnG2Q+VldXGYaE\nhBQUFGD4MAAAQE/0bo5VW1sbEhLi4eHh4+Njb2/PnqkRUXV1tVAoDAwM9PPzc3R09PPzq6qq4jju\n7NmzXl5eRkZGVVVVnp6eOjo6VlZWt27dIqITJ05oa2sLBILg4GDWOJsO5/Dhw9OnTzcwMBA9b3Nz\n84wZM9jn+vp6Pz8/Ly+v4ODgLVu29LDH6OrVq0RkZGTEl7BRID///DMRpaWlGRkZpaend6546tQp\nIhJNzkQtXbqUfZDnnenKsmXLlJSULl68KNEdAQAAgFeS7hGjJGMampub7e3tPTw82traOI6LiYkh\nosTExJqaGjMzs23btrHDysvLzczMjI2Nnz9/XlxczBYA3r59e1FRUWxsLBHZ2dmxIw8dOkRE586d\nY5uPHj1asWJF5/NevXpVQ0Pjxx9/5DiupaXFzs5u/fr1bPzTgwcPlJWVu3XV9H/HY1lbWxNRfX09\nX8KStilTpnAcd+bMmQEDBiQmJnZux9bWlohevHgh5lzyvDOvvDqegYGBtra2mFBFW8B4LOhHMB4L\nAOSmF+cg3bdvn7+/f05ODputu7W1NTY29s9//nN4eHhYWFhpaSm/stjXX3/t6ekpFAp37949bty4\nvLw81jLHcQYGBi9evGBvujU3N7/99tuTJk1KTEwkoq1bt/7lL3+ZPHmy6EnZg7wPP/xwxYoVRBQZ\nGfnxxx/fu3fPwsKCHTB27Nj8/HzJr1ogEIwbNy4nJ4dtzpw588qVKw0NDWz8OBE1NDQMHDjQxsbm\nxx9/ZAGoqKh0bmfKlCnXr18XverO/vrXv8rtzrzy6ngjR45sa2srLS2V5P5MmTLljXyli73M2OGt\nUujviouLs7KypPu7BwDQLb34rJCtMcI/VlNRUVmzZo2WllZGRgYRDR48mD+SrY/LlncVXTtWIBBo\naWmx9WWJSE1N7ZNPPklOTi4oKGhubs7Nze2QRhDR559/PmfOHD6NYA+8jI2N+QOUlHp0yWyZPNEZ\ntthKtMOHD+cv85UVWZJ3//59MY3L886I0dzcXF5ePmnSpNceCQAAAF15dUIgE2xN3Pz8/A7f1izL\nKSoqsrKyYiX6+vpENHTo0Ne2uX79+s8++ywyMnLq1KnLli3rsDcpKWnQoEH8cnhEVFJSQkTPnj0T\nHUHVE5aWlkRUWlrKj3Nia/q+diVae3v7o0ePZmVlvfvuu10dI887I0ZaWlpLS0tXQ8c68/X17aW1\ndBRL8rV0oB9hffCKjgIAfhd6sR+LDV0KDQ1tb29nJUVFRefOnWN9MykpKfyRxcXFROTg4PDaNocO\nHbp+/fqYmJj4+PglS5aI7rp48WJJSYloGnHt2jXW7SR6rh5atWrV0KFDL1++zJekpaWpqqq6u7uz\nzdbW1ldW9PDwsLGxOXjwYOcHcI2NjceOHaPfeq3kc2e6aqepqSkoKGjSpEk+Pj6vPSkAAAB0pRdz\nrMDAwIEDB37//fcODg5RUVFbt27dsWPHvHnzAgICLC0tIyIiWA8QEUVFRU2bNs3b25uIGhsbiYgf\nLfHy5Usiamlp4Zv18fGpra2dPHmyqqoqX5iamrpz587W1tbIyMjIyMiIiAhfX99z584JhUJlZeWg\noKB///vfDQ0NaWlpLMV5+PChJJdQX19PRG1tbXyJtrb2li1bvvzySxZYTU3N4cOHg4OD2YCk5OTk\nYcOGnT9/vnNTysrK33zzjYaGxvTp00+dOsVSsfr6+rS0tAULFrBcUJ53hr861ixz+/btuXPnVlVV\nxcXFiTYCAAAA3dWLzwpNTEyysrI+/fTTGzdu5Obmuri47N69W0lJaeDAgZmZmSEhIZ6enhMmTFBW\nVtbV1WW9QVFRUWxq9dDQ0I8//vjo0aMsJdq6deu2bdsGDBhARMbGxh9//PHGjRv5E127ds3Z2bmh\noUG0e4mICgoKTExM0tLStmzZ4uLioqent2HDhkmTJllYWBQWFo4aNYq9Y9iVy5cvHz9+nIiKiop2\n797t6OjIHnoGBATo6ur+z//8z6hRo/Ly8gICAtavX8+qqKurDxkyhJ/Vs4Px48f/8ssvX3zxxb/+\n9S9/f/9BgwapqKgsWLAgISGBTZolzztz9erVo0ePEtGjR49mzZqlrq6urq6uqqrq5ubm6empqakp\n2Q8ZAAAAXq0X3yuE3wOBQBAfH4/xWNBf4G8XAMjNmzDPu3QEXes8lwEAAABAt/Tis8I+Dv+RBejL\nCgsLk5KSmpqalixZYmZmpuhwAAC67ffbjwUgW3l5eeHh4fHx8dbW1gKBwNLSkr1VwKSmpjo5OQkE\nAltbWzbVuJyVlJTExMS4urqKmVU1IiJCdBo2hbRJRDU1Nd7e3nPnzp04caJQKOycYIm22draGhAQ\n8OTJk26dAgBAHqSbHh7rUQBDvbmWzuPHjxXYSLfW0rl8+fKKFSuampo4jquurmb/uDZs2CB6DHub\nNScnR7p4eo69NvHK1ZM4jrtx4wZ7eUKxbZaXl0+ePNnMzKyiokLCNisrK5csWVJQUCBJ+/jbBQBy\ng34s6KMePnzIzzqm2EZeKzs7e/Xq1REREWpqakQ0ZMgQIpoxY0Z0dLRol9WIESPo/646IGejRo3q\naldVVdXZs2elWBNJtm1yHPf+++///PPPX3/99VtvvSVhmzo6Otu2bXN2dq6trZX8XAAAvQ05FvRF\nT548Wbhw4a+//qrwRl6rvb3dw8NjzZo1bAIOXnx8vIGBgZeXV2FhISthU46xPKxP4TguJCREKBR2\n96GezNtMTk4+f/68k5PTlClTutWmtbW1qampUCjsUdAAADKFHAt6XXV1tVAoDAwM9PPzc3R09PPz\nY4s8fvnll+xFTiKqqanZu3cvv3ns2LHs7OyysrIPP/yQ47jMzEx/f/8xY8aUlZUtXbpUW1vbysrq\n5MmTkjfCIklLSzMyMkpPT5fh1SUmJt65c2fevHkdyg0NDRMSEurq6pYvX86vLPna28Jx3NmzZ728\nvIyMjKqqqjw9PXV0dKysrG7dusVqNTQ07Nq1a926dba2tg4ODnfv3u35JURERLi5uUmyZFNvt/nV\nV18R0ahRo2bOnKmpqWljY5OcnCxhm05OTtHR0QUFBT0JGwBAlqR7xIgxDcDQ68Zj1dTUmJmZbdu2\njW2Wl5ebmZkZGxuzfMLExET0F0l0k34b5dPa2pqUlKShoUFE3t7e6enpcXFxbJbUq1evStgIc+bM\nmQEDBiQmJkpyaRKOx1q+fDkRNTc3ixbyAezbt4+I/P39O5R3dVueP39eXFw8aNAgItq+fXtRUVFs\nbCwR2dnZsSPXr19///599nnu3Ll6enrV1dWSXA4fQIexU9euXdu7dy/7PG7cOCn+XcuwzdGjRxNR\neHh4aWlpZmYmW2b0+vXrkrR5+/ZtIgoLCxN/CvztAgC5QY4FPfLaHCsoKIiISktL+RLWVyEUCrlO\nX5aimx2+udnLZbW1tWxz//79RMQmk5S8EY7jWlpaJLw0CXOs0aNHDx06tEMhH0B7eztbojslJUW0\nXPxtGTt2rGgLenp6ampqHMdlZWV1/m9SUlKShFfEdbohlZWVa9eubWtrY5syybF60qa6urqBgQG/\nyfLLlStXStImWwB+/vz54k+Bv10AIDe/3/mxQD4yMjKIaPDgwXwJW/pazLrUr6SkpERErIOHiJyd\nnX19ffPz87sbj4qKjH/ny8rKDA0Nu9orEAhiYmJ++eUXT0/Pn3/+mS8Xf1tExxsJBAItLa2Kigoi\nunnzpoWFxb1792QV/MaNGzdu3JiXl8c2m5qaiCgnJ0dVVdXU1FT+bRoYGPBLyBPR7NmziSg3N1eS\nNocNG0ZE5eXl0oUNACBzyLGgd7HcqKioyMrKipXo6+sTUQ9H/wwfPpyIpHgPTuaUlZVFVw3vbPDg\nwSdPnrSzs1u5ciVfKN1tefbs2cOHD+vq6vhck4ja2trEr7wpRmJi4okTJzoUjh8/3tTU9MGDB/Jv\n08zM7MqVKxzHsSxTV1eXiLS1tSVpk1XhMLcwAPQZGPMOvYt1z6SkpPAlxcXFROTg4EC/fS+ybon2\n9nY2sxT/Ndna2tpVs8+ePZOuETFtSsfQ0PDFixeiJSzlEk28LCwsYmJifvjhB75E/G3pirm5ORvz\nzpdkZ2dHRkZKHXxjY6Notzb/DE7qBKuHbbq7uzc1Nf30009ss7Kykojs7OwkaZO9SCGmTxEAQM6Q\nY0HvCggIsLS0jIiIePr0KSuJioqaNm2at7c3EZmbmxPR9u3b8/PzDx48yPKkCxcutLW1mZqaPn36\n9PHjx6Kt8RnSpUuXbGxsPvjgg241kpycPGzYsPPnz8vwAu3t7V++fPny5Uu+hD3X6/DQytXVddOm\nTRLelsbGRhJJE1njLS0tixcvNjY2DgkJWbduXVxcXHBw8KZNm9asWUNE4eHhFhYWx48fFxMqm3de\nfK+bKPm3uWrVKktLyz179rBrP336tL6+vp+fnyQts4Rs+vTpEkYCANDbkGNB7xo4cGBmZqa7u7un\np6e/v39AQICurm5aWhqbLGrXrl12dnb79u376KOPFixYYGlp6eHh8eLFi9bWVhcXlyFDhty8eVO0\ntQMHDlRWVlZUVJSWlqanp3e3EXV19SFDhqirq8vwAlevXk1EmZmZbPPUqVPr1q0jIi8vrytXroge\nuXv37nfeeee1tyUqKopNnh4aGlpdXX3gwIHS0lIi2rp1K8dxaWlpixYtOn36tL+/f0VFRVxcHJvy\ntLCwMCcn59NPP+0qzsuXL7Mkr6ioaPfu3XxfkRjyb1NFReXKlSsaGhqenp7BwcFZWVm3bt3S0tJ6\nbbNElJGRoaSk5ObmJsnBAAByIJBu+EJCQgJ7pUvmAUH/IhAI4uPjXV1de/tE5ubmubm58vyVYxeV\nkJDw2iPfe++9sWPHHjhwoPeDEicvL2/VqlXXr1//HbZJRIsWLTIwMIiOjhZ/GP52AYDcoB8LoKeO\nHj2akpJSVlamwBjq6uoOHTp05MiR32GbRJSZmZmXl7d3717ZNgsA0BPIsaB/qKurI6K+uSCdvr7+\nyZMnfX19WZAKUVhYGBYWNmHChN9hmyUlJaGhoampqeyxKQBAH4EcC/q62traoKCgJ0+eEJGPjw8/\n8qlPmThxYmhoaFRUlKICmDBhgswzjH7RZktLS2xs7LffftsXJvIAABCF+bGgr9PU1AwLCwsLC1N0\nIK9hYmISEBCg6Ch+d1RVVQMDAxUdBQDAK6AfCwAAAED2kGMBAAAAyB5yLAAAAADZQ44FAAAAIHvI\nsQAAAABkT8p53k+dOrV06VKZRwMA0NtUVFRaWloUHQUAvPmkzLEaGxvPnTsn+UKwAJLLzMzcv3+/\nJIvYAEjBwMBgxowZio4CAN58UuZYAL0HK8oBAMAbAOOxAAAAAGQPORYAAACA7CHHAgAAAJA95FgA\nAAAAsoccCwAAAED2kGMBAAAAyB5yLAAAAADZQ44FAAAAIHvIsQAAAABkDzkWAAAAgOwhxwIAAACQ\nPeRYAAAAALKHHAsAAABA9pBjAQAAAMgeciwAAAAA2UOOBQAAACB7yLEAAAAAZA85FgAAAIDsIccC\nAAAAkD3kWAAAAACyhxwLAAAAQPaQYwEAAADIHnIsAAAAANlDjgUAAAAge8ixAAAAAGQPORYAAACA\n7CHHAgAAAJA95FgAAAAAsoccCwAAAED2kGMBAAAAyB5yLAAAAADZQ44FAAAAIHsqig4AgIiosrKy\npqaGfS4vLyeiwsJCfq+hoeGAAQMUExkAAIBUBBzHKToGANLW1q6qqupq74cffviPf/xDnvEAAAD0\nEJ4VQp8wbdo0JaUufxunTZsmz2AAAAB6DjkW9AkeHh5ddamqq6svWbJEzvEAAAD0EHIs6BOcnZ01\nNDQ6l6uoqDg7O2tqaso/JAAAgJ5AjgV9wsCBA//85z+rqqp2KG9ra1u5cqVCQgIAAOgJ5FjQV7i7\nu7e0tHQoHDRo0Lx58xQSDwAAQE8gx4K+wsnJaciQIaIlqqqqbm5u6urqigoJAABAasixoK9QVVVd\nsWKFmpoaX9LS0uLu7q7AkAAAAKSG+bGgD0lPT581axa/qaurW1ZWpqysrLiIAAAApIR+LOhDZsyY\noa+vzz6rqqquWrUKCRYAAPRTyLGgD1FSUvLw8GCPC1taWlasWKHoiAAAAKSEZ4XQt/z444+2trZE\nNHLkyEePHgkEAkVHBAAAIA30Y0Hf8oc//MHU1JSI3n//fSRYAADQf6lIV62oqGjLli1tbW2yjQaA\niNhkDTdu3HB1dVV0LPAGMjIy2rdvn6KjAIA3n5T9WDdu3Pjuu+9kGwr0RydOnCguLpZtm2+//fYf\n/vCHDnNlyV9mZmZmZqZiYwCZKy4u3r9/v6KjAIDfBSn7sZiEhARZxQH9lEAg8PX1fSM7nNhF4Zf8\nDZOQkODm5qboKADgdwHjsQAAAABkDzkWAAAAgOwhxwIAAACQPeRYAAAAALKHHAsAAABA9nr0XiEA\nQC8pLCxMSkpqampasmSJmZmZosMBAOg29GOBAvzpT38SCoWKjkLG8vLywsPD4+Pjra2tBQKBpaVl\nfX09vzc1NdXJyUkgENja2sbHx8s/vJKSkpiYGFdX16lTp3Z1TERERLfm1u+NNomopqbG29t77ty5\nEydOFAqFnRMs0TZbW1sDAgKePHnSrVMAAMgDJxX2JSFdXXiTEFF8fHx3a7m5uQUHB/dGPMzjx497\n3oiLi4uLi4uEB1++fHnFihVNTU0cx1VXV7N/XBs2bBA95uHDh0SUk5PT89ik8+jRIyIaN27cK/fe\nuHFjwIAB3f13LfM2y8vLJ0+ebGZmVlFRIWGblZWVS5YsKSgokKR9/O0CALlBPxYowHfffRcSEtJL\njT98+NDd3b2XGn+l7Ozs1atXR0REqKmpERGboX7GjBnR0dGiXVYjRowgImNjY3nGJmrUqFFd7aqq\nqjp79uzIkSMV2ybHce+///7PP//89ddfv/XWWxK2qaOjs23bNmdn59raWsnPBQDQ25BjwRvlyZMn\nCxcu/PXXX+V2xvb2dg8PjzVr1ujo6IiWx8fHGxgYeHl5FRYWshJVVVUiYnlYn8JxXEhIiFAolOEi\n3NK1mZycfP78eScnpylTpnSrTWtra1NT0zfvATQA9GvIsUCu2traEhISPD09Z86cyXHc2bNnvby8\njIyMqqqqPD09dXR0rKysbt26xXFcZmamv7//mDFjysrKli5dqq2tbWVldfLkSSL68ssvBQIB+6Kt\nqanZu3cvv3ns2LHs7OyysrIPP/yQnTEtLc3IyCg9Pb2XrijjPOD/AAAgAElEQVQxMfHOnTvz5s3r\nUG5oaJiQkFBXV7d8+fLm5ubOFaurq4VCYWBgoJ+fn6Ojo5+fX1VVlZh7wmo1NDTs2rVr3bp1tra2\nDg4Od+/e7fklREREuLm5DR06tOdN9bDNr776iohGjRo1c+ZMTU1NGxub5ORkCdt0cnKKjo4uKCjo\nSdgAALIk3SNGjGkAhro/HosfwdPe3l5cXDxo0CAi2r59e1FRUWxsLBHZ2dm1trYmJSVpaGgQkbe3\nd3p6elxcnKamJhFdvXqV4zgTExPR30DRTfq/w4POnDkzYMCAxMTE7l6ahOOxli9fTkTNzc2ihXww\n+/btIyJ/f/8O5TU1NWZmZtu2bWOb5eXlZmZmxsbGz58/7+qesCPXr19///599nnu3Ll6enrV1dWS\nXxR1Gjt17dq1vXv3ss/jxo2T4t+1DNscPXo0EYWHh5eWlmZmZhoZGRHR9evXJWnz9u3bRBQWFib+\nFPjbBQBygxwLekSKHKu9vV30W3ns2LH871J7e7uenp6amhrbZC+U1dbWss39+/cTkZubG9fpW1Z0\ns/NXfktLS7cvTOIca/To0UOHDu1QKHpFy5YtI6KUlBTR8qCgICIqLS3lq7AuHKFQyHV9T7Kysjr/\nNykpKUnyi+pwcyorK9euXdvW1sY2ZZJj9aRNdXV1AwMDfpPllytXrpSkzZKSEiKaP3+++FPgbxcA\nyA3mxwJ56zCYRnRTIBBoaWlVVFSwTSUlJSJinTpE5Ozs7Ovrm5+f390zqqj04u95WVmZoaFhV3sF\nAkFMTMwvv/zi6en5888/8+UZGRlENHjwYL5k5syZRHTt2jXq+p7cvHnTwsLi3r17sgp+48aNGzdu\nzMvLY5tNTU1ElJOTo6qqampqKv82DQwMWArOzJ49m4hyc3MlaXPYsGFEVF5eLl3YAAAyhxwL+o3h\nw4cTkRTvvvUqZWXltrY2MQcMHjz45MmTdnZ2K1eu5AtZ+lhUVGRlZcVK9PX1iUj8AKZnz549fPiw\nrq6OzzuJqK2tTVlZWbrgExMTT5w40aFw/PjxpqamDx48kH+bZmZmV65c4TiOZZm6urpEpK2tLUmb\nrArHcdKFDQAgcxjzDv3Gs2fPiMjBwYF++0Jl/Rnt7e1sSir++7W1tVW0YodN2TI0NHzx4oVoCUu5\nRBMvCwuLmJiYH374gS9hvVYpKSl8SXFxMf12dV0xNzdnY975kuzs7MjISKmDb2xsFO3W5p/BSZ1g\n9bBNd3f3pqamn376iW1WVlYSkZ2dnSRtVlVVEZGYPkUAADlDjgXy9vLlSyKqqalhm42NjSSSHrG9\nLS0t/PF8hnTp0iUbG5sPPviAiMzNzYlo+/bt+fn5Bw8eZMnWhQsX2traTE1Nnz59+vjxY1YrOTl5\n2LBh58+f76XLsbe3f/nyJQubYc/1Ojy0cnV13bRpE78ZEBBgaWkZERHx9OlTVhIVFTVt2jRvb2/q\n+p4sXrzY2Ng4JCRk3bp1cXFxwcHBmzZtWrNmDRGFh4dbWFgcP35cTKhs3nnxvW6i5N/mqlWrLC0t\n9+zZw6799OnT+vr6fn5+krTMErLp06dLGAkAQG9DjgVyVVdXFxYWRkRPnz7dt2/fjh072GuGoaGh\n1dXVBw4cKC0tJaKtW7c2NDSwKgcOHKisrKyoqCgtLU1PT2ezTO3atcvOzm7fvn0fffTRggULLC0t\nPTw8Xrx40dra6uLiMmTIkJs3b7Lq6urqQ4YMUVdX76UrWr16NRFlZmayzVOnTq1bt46IvLy8rly5\nInrk7t2733nnHfZ54MCBmZmZ7u7unp6e/v7+AQEBurq6aWlpqqqqUVFRXd0TjuPS0tIWLVp0+vRp\nf3//ioqKuLg4NuVpYWFhTk7Op59+2lWcly9fZkleUVHR7t27+b4iMeTfpoqKypUrVzQ0NDw9PYOD\ng7Oysm7duqWlpfXaZokoIyNDSUnJzc1NkoMBAORAIN3whYSEBPZ6l8wDgv5FIBDEx8e7urr2RuPm\n5ua5ubmK+jVjF5WQkPDaI997772xY8ceOHCg94MSJy8vb9WqVdevX/8dtklEixYtMjAwiI6OFn8Y\n/nYBgNygHwugp44ePZqSklJWVqbAGOrq6g4dOnTkyJHfYZtElJmZmZeXt3fvXtk2CwDQE8ixoO+q\nq6sjor6/CJ2+vv7Jkyd9fX1ZwApRWFgYFhY2YcKE32GbJSUloaGhqamp7LEpAEAf8XvMsTq8BQZ9\nUG1tbVBQ0JMnT4jIx8eHH+3UZ02cODE0NDQqKkpRAUyYMEHmGUa/aLOlpSU2Nvbbb7/ta5N6AAC8\nITkWx3FHjhyZNGmSpqamtbV1TExM5/EWjY2NoaGhU6dO7bB2b1dKSkpiYmJcXV2nTp0q+a7XxpmQ\nkLBw4cLJkyc7Ojo6Ozt/9NFHO3fu9Pf371Y73TrjK+/Mf/7zn/nz57Nl/mbPnj179mxbW1tnZ+cj\nR46wd/QUS1NTk18UJSYmprv3WSFMTEwCAgIUHcXvjqqqamBgIHqwAKAvkm56+L62HsXmzZtXrlwZ\nGRnp4+PDFrk7dOhQ58Pq6+vZO0oSNsuvrNetXV2pqKiYNWuWqalpVlZWe3s7x3FtbW2xsbHa2tpr\n166VvJ1uEXNnWC/RmDFj2GZbW9vZs2dNTEzefvvtX375RcL2qftr6fQXEq6lA/1LX/vbBQBvsDdh\nnvfi4uLi4uK4uDi2+d57782bN+/gwYMff/xxhyMHDBigp6fH5iqUxKhRo6TY9Urt7e2LFy/OycnJ\nz8/nO9KUlJQ8PDyMjIz++c9/dqs1CYm/MyNGjCAiflIDJSUlZ2fnP/7xjzY2NosXL7579+6AAQN6\nIyoAAIDfgzfhWeGjR49E3ydydHTU1dXl17zrI06dOpWZmRkYGNj5SeWsWbNcXFx646RS3BlDQ8OQ\nkJCCggK8ogUAANATvZtj1dbWhoSEeHh4+Pj42NvbHzhwgOM4IqqurhYKhYGBgX5+fo6Ojn5+flVV\nVRzHnT171svLy8jIqKqqytPTU0dHx8rK6tatW0R04sQJbW1tgUAQHBzMGv/iiy+UlJQOHz48ffp0\nAwMD0fM2NzfPmDGDfa6vr/fz8/Py8goODt6yZUuvvvmVlpZmZGSUnp7eedepU6eIaM6cOa+suHTp\nUvZBnnemK8uWLVNSUrp48WI3rx4AAABESPeIUZIxDc3Nzfb29h4eHm1tbRzHxcTEEFFiYmJNTY2Z\nmdm2bdvYYeXl5WZmZsbGxs+fPy8uLmaL3W7fvr2oqCg2NpaI7Ozs2JGHDh0ionPnzrHNR48erVix\novN5r169qqGh8eOPP3Ic19LSYmdnt379ejb+6cGDB2z1XMmvlLoedNV515kzZwYMGJCYmNj5YFtb\nWyJ68eKFmHPJ886IvzoDAwNtbW0xoYq2gPFY0I9gPBYAyE0v5ljsYVNOTg7bbGlpiYmJef78eVBQ\nEBGVlpbyR3711VdEJBQKOY4bO3Ys33J7e7uenp6amhrbbGpqGjly5KJFi9hmcHDw7du3O5y0paVl\n5syZ3377LduMiIggonv37vEHmJmZ9V6OxQJ45cF/+tOfOlx1Z/K8M+KvzsjIyNDQUEyooi0A9DuS\n/G4DAPRQL455/+GHH4jIyMiIbaqoqLDFazMyMoho8ODB/JEzZ84komvXrhGRQCDgywUCgZaWFj9+\nSE1N7ZNPPhEKhQUFBSNHjszNzZ08eXKHk37++edz5sxZsWIF22QPvIyNjfkDlJR69/Goisqrb6mF\nhcX169fv379vaGjYVV153hkxmpuby8vLHRwcXnsk4+vr2y/mVuiu/fv3E5Gvr6+iAwFZyszMZD9Z\nAIDe1os5Vnl5ORHl5+dPmjRJtJxlOUVFRVZWVqxEX1+fiIYOHfraNtevX//ZZ59FRkZOnTp12bJl\nHfYmJSUNGjQoMDCQLykpKSGiZ8+e8ameotjb2x89ejQrK+vdd9/t6hh53hkx0tLSWlpauho61tmU\nKVN6acy+Yp04cYKI3shL+z3j0PkKAPLSi5061tbWRBQaGtre3s5KioqKzp07x/pmUlJS+COLi4uJ\nSJKOk6FDh65fvz4mJiY+Pn7JkiWiuy5evFhSUiKaRly7ds3c3LzDuXpba2vrK8s9PDxsbGwOHjxY\nWlraYVdjY+OxY8fot14r+dyZrtppamoKCgqaNGmSj4/Pa08KAAAAXenFHCswMHDgwIHff/+9g4ND\nVFTU1q1bd+zYMW/evICAAEtLy4iIiKdPn7Ijo6Kipk2b5u3tTUSNjY0k8n/Nly9fElFLSwvfrI+P\nT21t7eTJk1VVVfnC1NTUnTt3tra2RkZGRkZGRkRE+Pr6njt3TigUKisrBwUF/fvf/25oaEhLS2Mp\nzsOHDyW5hPr6eiJqa2uTcFdycvKwYcPOnz/f+XhlZeVvvvlGQ0Nj+vTpp06dYqlYfX19WlraggUL\nWC4ozzvDXwJrlrl9+/bcuXOrqqri4uJEGwEAAIDu6sVnhSYmJllZWZ9++umNGzdyc3NdXFx2796t\npKQ0cODAzMzMkJAQT0/PCRMmKCsr6+rqpqWlqaqqRkVFsfnTQ0NDP/7446NHj7KUaOvWrdu2bWNT\nYhobG3/88ccbN27kT3Tt2jVnZ+eGhobLly+LBlBQUGBiYpKWlrZlyxYXFxc9Pb0NGzZMmjTJwsKi\nsLBw1KhR7B3Drly+fPn48eNEVFRUtHv3bkdHR/6hZ1e71NXVhwwZws/q2cH48eN/+eWXL7744l//\n+pe/v/+gQYNUVFQWLFiQkJDAJs2S5525evXq0aNHiejRo0ezZs1SV1dXV1dXVVV1c3Pz9PTU1NSU\n6GcMAAAAXRBINzohISHBzc0NIxtAIBDEx8e7uroqOhDZYxeVkJCg6EBAlvC3CwDk5k2Y5106gq7l\n5OQoOjoAAADo396E9Qqlg//IAihcYWFhUlJSU1PTkiVL2Nx1AABvjN9vPxaA3OTl5YWHh8fHx1tb\nWwsEAktLS/bCAZOamurk5CQQCGxtbdnsvvJkb2/fuSu3oKCg9yoyNTU13t7ec+fOnThxolAo7Jxg\nRURE8HPCtba2BgQEPHnypFuXBgCgWL/ffizo+4qLi0eOHNkXGumJH3744fDhw8eOHVNTU5s/f/7Q\noUOzs7M3bdp0+PBhdoCDg8Pbb79tbGwcFxc3btw4ecZ279696urqPXv26OrqspLr169nZGSYmpr2\nUkWmoqJi3rx5tbW1WVlZb731VucDbt68uXnzZn5TRUVl8+bNGzZsCA8PNzExkejaAAAUDTkW9FEP\nHz5cvXr1lStXFN5IT2RnZ69evfrOnTtqampENGTIECKaMWNGdHT0nDlz3Nzc2GEjRoyg/7sggXzc\nvXs3NTWVz5OIKD09XZJpV6WuSEQcx73//vs///xzRkbGKxOsqqqqs2fPjhw5Mi8vjy/U0dHZtm2b\ns7NzVlYW3nsFgH4BzwqhL3ry5MnChQt//fVXhTfSE+3t7R4eHmvWrGFzc/Di4+MNDAy8vLwKCwtZ\nCZuNjOVh8rR8+XLRPKmpqen06dOd1wmQYUUiSk5OPn/+vJOT05QpUzrv5TguJCREKBSKLh7FWFtb\nm5qaCoVCSc4CAKBwyLGg11VXVwuFwsDAQD8/P0dHRz8/v6qqKiL68ssv2SAeIqqpqdm7dy+/eezY\nsezs7LKysg8//JDjuMzMTH9//zFjxpSVlS1dulRbW9vKyurkyZOSN8IiSUtLMzIySk9Pl8+FJyYm\n3rlzZ968eR3KDQ0NExIS6urqli9f3tzc3LniK+8Yx3Fnz5718vIyMjKqqqry9PTU0dGxsrK6desW\nq9XQ0LBr165169bZ2to6ODjcvXu3uwFfuHDByMho/PjxvVqRrXQ+atSomTNnampq2tjYJCcn83sj\nIiLc3Ny6Wj/KyckpOjpa8lFfAACKJN1S0mxkrgzWpIZ+joji4+PFHFBTU2NmZrZt2za2WV5ebmZm\nZmxszJIGNraGP1h0k4jGjRvHcVxra2tSUpKGhgYReXt7p6enx8XFsadFV69elbAR5syZMwMGDEhM\nTJTk0lxcXFxcXCQ5sivLly8noubmZtFCPrZ9+/YRkb+/f4fyru7Y8+fPi4uLBw0aRETbt28vKiqK\njY0lIjs7O3bk+vXr79+/zz7PnTtXT0+vurq6WwG7u7t/9tlnUlxptyqOHj2aiMLDw0tLSzMzM9la\notevX+c47tq1a3v37mWHsaFpHerevn2biMLCwqQIksHfLgCQG+RY0COvzbGCgoKIqLS0lC9h3RhC\noZDr9D0qutkhPWLvndXW1rLN/fv3ExGbTFLyRjiOa2lpkfDSep5jjR49eujQoR0K+dja29vZw7WU\nlBTRcvF3bOzYsaIt6OnpqampcRyXlZXV+X9QSUlJkkdbV1enqal579697l5mdyuqq6sbGBjwmyxT\nXLlyZWVl5dq1a9va2lj5K3Mstsr7/PnzuxskD3+7AEBu8KwQeldGRgYRDR48mC9hS1+LWZf6lZSU\nlIiI9eIQkbOzMxHl5+d3Nx4VFfm951FWVqalpdXVXoFAEBMTY25u7unpKbpSuPg7JjpKSSAQaGlp\nsaeNN2/etLCw6PDPe+HChZJHe+7cuVGjRllYWHTjCqWqaGBgILoa5uzZs4koNzd348aNHh4eeXl5\nOTk5OTk5TU1NRJSTkyP6ZHDYsGFEVF5e3t0gAQDkDzkW9C6WGxUVFfEl+vr6RNTVgBsJDR8+nIgU\nOynDaykrK79yQXHe4MGDT5482dDQsHLlSr5Qujv27Nmzhw8f1tXViRaKP3sH8fHxEg5a72FFMzOz\niooK7rdJgNnYeW1t7cTExHfffXf8b9gdGD9+vJOTE1+XpZgcJhAGgP4AORb0LtYHk5KSwpcUFxcT\nkYODA/32lcl6LNrb26urq0nkG7S1tbWrZp89eyZdI2LalDlDQ8MXL16IlrCkRzT1sbCwiImJ+eGH\nH/gS8XesK+bm5mzMO1+SnZ0dGRkpYai1tbUpKSkSTr7Qw4ru7u5NTU0//fQT26ysrCQiOzu7xsZG\n0U44/lnhgwcP+LrsbQlDQ8PuxgkAIH/IsaB3BQQEWFpaRkREPH36lJVERUVNmzbN29ubiMzNzYlo\n+/bt+fn5Bw8eZHnShQsX2traTE1Nnz59+vjxY9HW+Azp0qVLNjY2H3zwQbcaSU5OHjZs2Pnz5+Vz\n7fb29i9fvnz58iVfUlFRQZ0edbm6um7atInfFH/HGhsbSSSDZI23tLQsXrzY2Ng4JCRk3bp1cXFx\nwcHBmzZtWrNmDRGFh4dbWFgcP35cTKiJiYmjR4+2tLQULeyliqtWrbK0tNyzZw+7itOnT+vr6/v5\n+Yk5C48lZNOnT5fkYAAAxUKOBb1r4MCBmZmZ7u7unp6e/v7+AQEBurq6aWlpbETOrl277Ozs9u3b\n99FHHy1YsMDS0tLDw+PFixetra0uLi5Dhgy5efOmaGsHDhyorKysqKgoLS1NT0/vbiPq6upDhgxR\nV1eXz7WvXr2aiDIzM9nmqVOn1q1bR0ReXl4dpkXdvXv3O++8wz6LuWNRUVGPHj0iotDQ0Orq6gMH\nDrCBXFu3buU4Li0tbdGiRadPn/b396+oqIiLi2NTnhYWFubk5Hz66adiQo2Pj3dxcekwJVUvVVRR\nUbly5YqGhoanp2dwcHBWVtatW7fEDFwTlZGRoaSkxM/dCgDQlwmkG9mQkJDAXumSeUDQvwgEgvj4\neFdX194+kbm5eW5urjx/5dhFJSQk9KSR9957b+zYsQcOHJBRUFLKy8tbtWrV9evX+35F8RYtWmRg\nYBAdHS11C/jbBQByg34sgF509OjRlJSUsrIyBcZQV1d36NChI0eO9P2K4mVmZubl5e3du1e2zQIA\n9BLkWNA/sDfmamtrFR1I9+jr6588edLX17fDG3/yVFhYGBYWNmHChL5fUYySkpLQ0NDU1FT2ABQA\noO9DjgV9XW1tbVBQ0JMnT4jIx8eHH97UX0ycODE0NDQqKkpRAUyYMEG6vET+FbvS0tISGxv77bff\n9vHZOgAARMlvPkYA6WhqaoaFhYWFhSk6EOmZmJgEBAQoOop+TFVVNTAwUNFRAAB0D/qxAAAAAGQP\nORYAAACA7CHHAgAAAJA95FgAAAAAstejMe8nTpyQVRzQf2VlZXWY5vvNwN5kxC/5GyYrK0vRIQDA\n74WU87xfuXLl3XfflefyugAAMmFkZMSW2QYA6FVS5lgAvQernQAAwBsA47EAAAAAZA85FgAAAIDs\nIccCAAAAkD3kWAAAAACyhxwLAAAAQPaQYwEAAADIHnIsAAAAANlDjgUAAAAge8ixAAAAAGQPORYA\nAACA7CHHAgAAAJA95FgAAAAAsoccCwAAAED2kGMBAAAAyB5yLAAAAADZQ44FAAAAIHvIsQAAAABk\nDzkWAAAAgOwhxwIAAACQPeRYAAAAALKHHAsAAABA9pBjAQAAAMgeciwAAAAA2UOOBQAAACB7yLEA\nAAAAZA85FgAAAIDsIccCAAAAkD3kWAAAAACyhxwLAAAAQPaQYwEAAADIHnIsAAAAANlDjgUAAAAg\ne8ixAAAAAGRPRdEBABARxcbGlpaWss///e9/iWjXrl383tmzZ9vZ2SkmMgAAAKkIOI5TdAwA9NZb\nbz1//lxVVbXzrqamJm9v74iICPlHBQAAIDU8K4Q+wc3NTVlZuelViMjFxUXRAQIAAHQP+rGgT8jI\nyJg+fford7311ltlZWVKSvj/AAAA9Cf43oI+Ydq0aUZGRp3L1dTU3n//fSRYAADQ7+CrC/oEgUDg\n4eHReTxWc3PzihUrFBISAABAT+BZIfQV//3vf62trTsUmpiYFBQUKCQeAACAnkA/FvQVEydOHDdu\nnGiJmpqap6enouIBAADoCeRY0IesWrVK9HFhc3Pz8uXLFRgPAACA1PCsEPqQwsLCt99+m/1OCgSC\niRMn/vTTT4oOCgAAQBrox4I+xMTExMbGRiAQEJGysjIeFAIAQP+FHAv6ltWrVysrKxNRW1ubq6ur\nosMBAACQEp4VQt9SVlY2YsQIjuPeeeedK1euKDocAAAAKaEfC/oWAwMDe3t7juPwoBAAAPo12fRj\n+fn57d+/v+ftAADInIqKSlpa2owZMxQdCAD8vqjIpJUnT55MmTLFz89PJq1BP5WZmbl///6EhIQe\ntsNxXFVVlba2tkyikhVXV1dfX9+pU6cqOhDoNldX16dPnyo6CgD43ZFNjkVEI0eOdHFxkVVr0B+x\nPtE3+NdgypQpb/DVAQCAbGE8FgAAAIDsIccCAAAAkD3kWAAAAACyhxwLAAAAQPaQYwEAAADInsze\nKwQA6K7CwsKkpKSmpqYlS5aYmZkpOhwAAFlCPxYo3p/+9CehUKjoKHpRXl5eeHh4fHy8tbW1QCCw\ntLSsr6/n96ampjo5OQkEAltb2/j4eDnHZm9vL+ikoKCg9yoyNTU13t7ec+fOnThxolAo7JxgRURE\nsNXBiai1tTUgIODJkyfdujQAAMVCPxYonrGxsYaGRu+1X1xcPHLkyN5rX7wffvjh8OHDx44dU1NT\nmz9//tChQ7Ozszdt2nT48GF2gIODw9tvv21sbBwXFzdu3Dh5xnbv3r3q6uo9e/bo6uqykuvXr2dk\nZJiamvZSRaaiomLevHm1tbVZWVlvvfVW5wNu3ry5efNmflNFRWXz5s0bNmwIDw83MTGR6NoAABQN\nORYo3nfffdd7jT98+HD16tWKWl46Ozt79erVd+7cUVNTI6IhQ4YQ0YwZM6Kjo+fMmePm5sYOGzFi\nBBEZGxvLOby7d++mpqbyeRIRpaenSzLPqtQViYjjuPfff//nn3/OyMh4ZYJVVVV19uzZkSNH5uXl\n8YU6Ojrbtm1zdnbOysrS1NSU5EQAAIqFZ4XwJnvy5MnChQt//fVXhZy9vb3dw8NjzZo1Ojo6ouXx\n8fEGBgZeXl6FhYWsRFVVlYhYHiZPy5cvF82TmpqaTp8+vWzZst6rSETJycnnz593cnKaMmVK570c\nx4WEhAiFQv5BIc/a2trU1PTNfqwMAG8S5FigSG1tbQkJCZ6enjNnzuQ47uzZs15eXkZGRlVVVZ6e\nnjo6OlZWVrdu3eI4LjMz09/ff8yYMWVlZUuXLtXW1raysjp58iQRffnll2wwEBHV1NTs3buX3zx2\n7Fh2dnZZWdmHH37IzpiWlmZkZJSeni6Hq0tMTLxz5868efM6lBsaGiYkJNTV1S1fvry5ublzxerq\naqFQGBgY6Ofn5+jo6OfnV1VVJeb+sFoNDQ27du1at26dra2tg4PD3bt3uxvwhQsXjIyMxo8f36sV\nv/rqKyIaNWrUzJkzNTU1bWxskpOT+b0RERFubm5Dhw59ZV0nJ6fo6GjJR30BACgSJwsuLi4uLi4y\naQr6LzZeu7u1Hj16RETjxo1rb28vLi4eNGgQEW3fvr2oqCg2NpaI7OzsWltbk5KS2Jgtb2/v9PT0\nuLg49sDo6tWrHMexMTp8m6KbrHF+15kzZwYMGJCYmNjdOIkoPj6+W1WWL19ORM3NzR3aYR/27dtH\nRP7+/h3Ka2pqzMzMtm3bxjbLy8vNzMyMjY2fP3/e1f1hR65fv/7+/fvs89y5c/X09Kqrq7sVsLu7\n+2effdatKlJUHD16NBGFh4eXlpZmZmYaGRkR0fXr1zmOu3bt2t69e9lhbGhah7q3b98morCwsG6F\nJ8XPDgCg55BjgcxIl2O1t7eLpkFjx47lG2lvb9fT01NTU2Ob7NWz2tpatrl//34icnNz4zp9H4tu\ndsixOI5raWnpbpCcVN/To0ePHjp0aOd22If29nb2cC0lJUW0PCgoiIhKS0v5KqzjRygUcl3fn6ys\nrM7/g0pKSpI82rq6Ok1NzXv37nXrGqWoqK6ubmBgwG+yTHHlypWVlZVr165ta2tj5a/MsUpKSoho\n/vz53YoQORYAKASeFYKCdRh2I7opEAi0tLT4p2lKSkZD3usAACAASURBVEpExDpyiMjZ2ZmI8vPz\nu3tGFRU5vepRVlampaXV1V6BQBATE2Nubu7p6VlaWsqXZ2RkENHgwYP5kpkzZxLRtWvXqOv7c/Pm\nTQsLiw7/vBcuXCh5tOfOnRs1apSFhUU3rlCqigYGBmz8GTN79mwiys3N3bhxo4eHR15eXk5OTk5O\nTlNTExHl5OSIPhkcNmwYEZWXl3c3SAAA+UOOBf3V8OHDiUiBkzK8lrKycltbm5gDBg8efPLkyYaG\nhpUrV/KFLJUsKiriS/T19YmoqyFKzLNnzx4+fFhXVydaKP7sHcTHx0s4aL2HFc3MzCoqKjiOY5ts\n7Ly2tnZiYuK77747/jfsDowfP97JyYmvy1JMvi4AQF+GHAv6q2fPnhGRg4MD/fbVy3o+2tvbq6ur\nSeSbuLW1VbRih83eY2ho+OLFC9ESlvSIpj4WFhYxMTE//PADX8J6rVJSUviS4uJi+u1Ku2Jubs7G\nvPMl2dnZkZGREoZaW1ubkpIi4eQLPazo7u7e1NT0008/sc3KykoisrOza2xsFO2E458VPnjwgK9b\nVVVFRIaGht2NEwBA/pBjgYK9fPmSiGpqathmY2MjiaRHbG9LSwt/PJ8hXbp0ycbG5oMPPiAic3Nz\nItq+fXt+fv7BgwdZsnXhwoW2tjZTU9OnT58+fvyY1UpOTh42bNj58+flcGn29vYvX75kl8BUVFRQ\np0ddrq6umzZt4jcDAgIsLS0jIiKePn3KSqKioqZNm+bt7U1d35/FixcbGxuHhISsW7cuLi4uODh4\n06ZNa9asIaLw8HALC4vjx4+LCTUxMXH06NGWlpaihb1UcdWqVZaWlnv27GFXcfr0aX19fT8/PzFn\n4bGEbPr06ZIcDACgWMixQJHq6urCwsKI6OnTp/v27duxYwd7zTA0NLS6uvrAgQNsoNLWrVsbGhpY\nlQMHDlRWVlZUVJSWlqanp7ORPbt27bKzs9u3b99HH320YMECS0tLDw+PFy9etLa2uri4DBky5ObN\nm6y6urr6kCFD1NXV5XB1q1evJqLMzEy2eerUqXXr1hGRl5dXhzlRd+/e/c4777DPAwcOzMzMdHd3\n9/T09Pf3DwgI0NXVTUtLU1VVjYqK6ur+cByXlpa2aNGi06dP+/v7V1RUxMXFsSlPCwsLc3JyPv30\nUzGhxsfHu7i4dBgb10sVVVRUrly5oqGh4enpGRwcnJWVdevWLTED10RlZGQoKSnxc7cCAPRlApmM\nbHB1dSWihISEnjcF/VdCQgJ7y6+X2jc3N8/NzVXUWByBQBAfH89+1SX33nvvjR079sCBA70UlYTy\n8vJWrVp1/fr1vl9RvEWLFhkYGERHR3erlnQ/OwCAHkI/FkAvOnr0aEpKSllZmQJjqKurO3To0JEj\nR/p+RfEyMzPz8vL27t0r22YBAHoJcizqMCoZ+iz20lxtba2iA+kGfX39kydP+vr6dnjjT54KCwvD\nwsImTJjQ9yuKUVJSEhoampqayh6AAgD0fW9mjsVx3JEjRyZNmqSpqWltbR0TE9P5AVNjY2NoaOjU\nqVM7rCXXlZKSkpiYGFdX16lTp3b3XGLiTEhIWLhw4eTJkx0dHZ2dnT/66KOdO3f6+/tL2EJ3dRXt\nf/7zn/nz57MlaGbPnj179mxbW1tnZ+cjR46w8eOKVVtbGxQU9OTJEyLy8fHhRzj1CxMnTgwNDY2K\nilJUABMmTJAuL5F/xa60tLTExsZ+++23fXmqDgCAjmQyk2lfm+d98+bNK1eujIyM9PHxYQuwHDp0\nqPNh9fX1bKSthM3yq75Ica7OKioqZs2aZWpqmpWV1d7eznFcW1tbbGystrb22rVrJQypu8REyzKY\nMWPGsM22trazZ8+amJi8/fbbv/zyiySNSzfPe39BmCu838LPDgAU4g3MsR4/fuzu7s5v/vvf/yYi\nU1PTVx78yvU6xOiQY3XrXKLa2tqmTp2qpaVVWVnZYdfly5fZyHGZe220nTPI0tJSAwMDU1PT+vr6\n17aPHAv6JvzsAEAh3sBnhY8ePRIdFevo6Kirq8vmJeo75zp16lRmZmZgYGDnJ5WzZs2SYipISUgR\nraGhYUhISEFBAQYaAwAAdItcc6za2tqQkBAPDw8fHx97e/sDBw5wHEdE1dXVQqEwMDDQz8/P0dHR\nz8+vqqqK47izZ896eXkZGRlVVVV5enrq6OhYWVndunWLiE6cOKGtrS0QCIKDg1njX3zxhZKS0uHD\nh6dPn25gYCB63ubm5hkzZrDP9fX1fn5+Xl5ewcHBW7Zs6eFIZPHnSktLMzIySk9P71zx1KlTRDRn\nzpxXNrt06VL2QZ53pivLli1TUlK6ePHia+8GAAAA/P9k0hsmybPC5uZme3t7Dw+PtrY2juNiYmKI\nKDExsaamxszMbNu2beyw8vJyMzMzY2Pj58+fFxcXswWAt2/fXlRUFBsbS0R2dnbsyEOHDhHRuXPn\n2OajR49WrFjR+bxXr17V0ND48ccfOY5raWmxs7Nbv349G//04MEDZWXlbt0E6vQ0ratzcRx35syZ\nAQMGJCYmdj7S1taWiF68eCHmXPK8M+KvzsDAQFtbW0yoDJ4VQt+Enx0AKISK3JK5iIiI9PT0nJwc\ntuTtqlWriGj69Ok7d+7Mz89nK6IQkZ6eXnBwsKen544dO3bv3j1ixIi8vLy//vWvRDRq1Ch/f39+\nmbMPPvhgz549//jHP+bPn09E0dHRQqGww0lbW1uDgoJiYmJsbGyI6J///OeNGzeOHj3KpqU2NTU1\nMTHJz8+XyQV2OBcRLV68uKamRkXlFTeZ5Xb19fViFvqV550RT0VFpcNE3mKcOHFCwiP7naysLMnv\nAwAA/M7JL8diq94aGRn9fydWUWGLqWVkZBDR4MGD+SPZmrjXrl2j39b6ZQQCgZaWFj9+SE1N7ZNP\nPhEKhQUFBSNHjszNzZ08eXKHk37++edz5sxZsWIF22QPvIyNjfkDWMInEx3OxV/mKw+2sLC4fv36\n/fv3xaxuK887I0Zzc3N5ebn4BYlFvcGzae/fv3///v2KjgIAAPoH+Y3HYuvgdu40YllOUVERX6Kv\nr09EYjp4eOvXrx80aFBkZOSZM2eWLVvWYW9SUtKgQYP+9re/8SUlJSVE9OzZM+kuQYzO5xLP3t6e\niLKyssQcI887I0ZaWlpLS0tXQ8c6U3TXbG8hPG/qtyT81QUAkC355VjW1tZEFBoa2t7ezkqKiorO\nnTvH+mZSUlL4I4uLi4lIko6ToUOHrl+/PiYmJj4+fsmSJaK7Ll68WFJSEhgYyJdcu3bN3Ny8w7lk\n4pXnYh9aW1tfWcXDw8PGxubgwYNsTV9RjY2Nx44do996reRzZ7pqp6mpKSgoaNKkST4+Pq89KQAA\nAPDkl2MFBgYOHDjw+++/d3BwiIqK2rp1644dO+bNmxcQEGBpaRkREfH06VN2ZFRU1LRp07y9vYmo\nsbGRiPj/ib58+ZKIWlpa+GZ9fHxqa2snT56sqqrKF6ampu7cubO1tTUyMjIyMjIiIsLX1/fcuXNC\noVBZWTkoKOjf//53Q0NDWloaS3EePnwoySXU19cTUVtbm2hhV+ciouTk5GHDhp0/f75zU8rKyt98\n842Ghsb06dNPnTrFUrH6+vq0tLQFCxawXFCed4a/OtYsc/v27blz51ZVVcXFxYk2AgAAAK8nk654\nCecg/e9//+vo6Dhs2LDhw4d/8skn/Ft1NTU1QqFw7ty5fn5+QqHw73//e2NjI8dxkZGRLMiQkJAX\nL17wQ2E2b94sOiXmJ598IjqTZ0ZGxoABAzpfaUFBAcdx6enp06ZN09TUNDEx2bFjx4wZMz744IPU\n1NTW1lbxwaelpW3YsIGIVFRUdu3adefOndee6+LFi4aGhpcuXeqqzZqamp07d7733ntjxoyxtLS0\ntrYOCgoSvRa53ZkrV66sXbuWbdrb2zs6Oi5atOgvf/lLZGTky5cvX/uTZfBeIfRN+NkBgEIIOFkM\nVmDDnBMSEnreFPRfCQkJbIZ6RQfSKwQCQXx8/Bs8ov8Nhp8dACjEGzjPu3QEXcvJyVF0dAAAANDP\nyG/uhj7uTe19AQAAAIVAPxYAAACA7CHHApC9vLy88PDw+Ph4a2trgUBgaWnJXttkUlNTnZycBAKB\nra0te1FAUSIiIqSbub67FUtKSmJiYlxdXadOnSpaznHckSNHJk2apKmpaW1tHRMTw/cocxz39ddf\nL1q0aMuWLbNnz964cWNVVRURtba2BgQEPHnyRIqwAQDkSiYj5yV8rxDebL39XuHjx48V2AhJ/G7a\n5cuXV6xY0dTUxHFcdXU1+4e2YcMG0WPYdCE5OTnSBSMTN27cYO+Zyqfio0ePqNOCmJs3b165cmVk\nZKSPj4+GhgYRHTp0iO36xz/+QUQpKSkcx/3yyy9EtHjxYrarsrJyyZIl7O1dSUj+swMAkCH0Y0H/\n8PDhQ3d3977QiHjZ2dmrV6+OiIhQU1MjoiFDhhDRjBkzoqOjRbusRowYQf93WSc5q6qqOnv27MiR\nI+VWcdSoUR1KiouLi4uLv/nmm48++ujgwYNnzpwhooMHD7K9X3/9NRH98Y9/JCILCwtdXd1Lly6x\nXTo6Otu2bXN2dq6tre1uGAAAcoMcC/qBJ0+eLFy48Ndff1V4I+K1t7d7eHisWbNGR0dHtDw+Pt7A\nwMDLy6uwsJCVsDldWR4mfxzHhYSECIXC7j4olLriKz169Gjv3r38pqOjo66uLr/spra2Nv22zmld\nXd3z58/fffdd/mBra2tTU9POi50DAPQdyLFA3qqrq4VCYWBgoJ+fn6Ojo5+fHxtn8+WXX7LJMoio\npqZm7969/OaxY8eys7PLyso+/PBDjuMyMzP9/f3HjBlTVla2dOlSbW1tKyurkydPSt4IiyQtLc3I\nyCg9PV1Wl5aYmHjnzp158+Z1KDc0NExISKirq1u+fHlzc7OE94TjuLNnz3p5eRkZGVVVVXl6euro\n6FhZWd26dYvVamho2LVr17p162xtbR0cHO7evSthnBEREW5ubpIsfCmriq80ffp0AwMD0ZLm5uYZ\nM2awz/v37zcxMdm0adOjR48iIyOFQuG3334rerCTk1N0dHRBQYFMggEAkD2ZPHHEeCzgJBuPVVNT\nY2Zmtm3bNrZZXl5uZmZmbGzMUgoTExPRFkQ36behPK2trUlJSWzsjre3d3p6elxcnKamJhFdvXpV\nwkaYM2fODBgwIDExUZKrIwnG9CxfvpyImpubO1RkH/bt20dE/v7+Hcq7uifPnz8vLi4eNGgQEW3f\nvr2oqCg2NpaI7Ozs2JHr16+/f/8++zx37lw9Pb3q6urXXsi1a9f27t3LPo8bN07yPwJSV+RRp/FY\noq5evaqhofHjjz/yJRUVFdOmTRsxYoSvr2/n42/fvk1EYWFhkpwX47EAQP6QY4HMSJJjBQUFEVFp\naSlf8tVXXxGRUCjkOn1zi252+Ho2MzMjotraWrbJVhNis8xL3gjHcS0tLRJenSTf06NHjx46dGjn\niuxDe3v7smXL6Ldx3Hy5+HsyduxY0Rb09PTU1NQ4jsvKyur8X6akpCTxEVZWVq5du7atrY1tSp4q\nSV1RlJgcq6WlZebMmd9++61oYVFR0YIFC1i/4KeffsqfnSkpKSGi+fPnS3Je5FgAIH94VghylZGR\nQUSDBw/mS2bOnElE165d61Y7SkpKRMT6eIjI2dmZiPLz87sbj4qKLKfhLSsr09LS6mqvQCCIiYkx\nNzf39PRki5Ez4u+J6MgngUCgpaXFnjbevHnTwsKiw7/nhQsXio9w48aNHh4eeXl5OTk5OTk5TU1N\nRJSTk/PaJ25SV5TQ559/PmfOnBUrVvAl169f/8Mf/uDp6XnmzJlp06aFh4f/7W9/E60ybNgwIiov\nL5dJAAAAMoccC+SK5UZFRUV8ib6+PhH1cIjP8OHDiUiKl91kS1lZua2tTcwBgwcPPnnyZENDw8qV\nK/lC6e7Js2fPHj58WFdXJ1oo/uxElJiY+O67747/DTvp+PHjnZyceqmiJJKSkgYNGtQhhdqyZcuz\nZ89mzZqlrq7+3XffEdHhw4dFD2DZJ4cVGgCgr0KOBXLFemhSUlL4kuLiYiJycHCg3741WR9Je3s7\nm1yK/xJtbW3tqtlnz55J14iYNqVgaGj44sUL0RKW9IimPhYWFjExMex1OUb8PemKubk5G/POl2Rn\nZ0dGRoqPsLGxUbTfi3/k9+DBg16q+FoXL14sKSkJDAzkS1gHHuuuY69ejhw5Uk9Pr8PLjOxVCUND\nwx4GAADQS5BjgVwFBARYWlpGREQ8ffqUlURFRU2bNs3b25uIzM3NiWj79u35+fkHDx5kedKFCxfa\n2tpMTU2fPn36+PFj0db4DOnSpUs2NjYffPBBtxpJTk4eNmzY+fPnZXV19vb2L1++fPnyJV/CZiLo\n8DzL1dV106ZNEt6TxsZGEskRWeMtLS2LFy82NjYOCQlZt25dXFxccHDwpk2b1qxZQ0Th4eEWFhbH\njx/vbvy9WpHNdN+hpy01NXXnzp2tra2RkZGRkZERERG+vr7nzp0jIjaTGfv86NGjiooK9koBr7Ky\nkoimT5/e3WgBAOREJqO6MOYdOInnea+pqREKhXPnzvXz8xMKhX//+9/5PpLc3Fw7O7uBAwfOnTs3\nNzd3+vTpHh4ex48fb2xsDAwMNDAw+P7779mRrB9lz549v/76a3l5+Y4dO16+fNndRi5evGhoaHjp\n0iVJro4kGDfNeqcuXLjANk+ePDl//nwiWrBgwf/+7/+KHtnc3PzOO++89p7w/VIhISEvXrxgQ/uJ\naPPmzfX19Q8fPly0aJGWlpa+vv6GDRsqKipYaxs3bhQIBMOHD3/tRXUYut57FdPS0jZs2EBEKioq\nu3btunPnDsdxGRkZbL74DtgE7u3t7ZGRkX/84x/9/Pz+/Oc/b926taGhQbTNL774QklJSZLZ3iX5\n2QEAyJyAk8VoBldXVyJKSEjoeVPQfyUkJLA3++RwLnNz89zcXPmcixEIBPHx8exXXYz33ntv7Nix\nBw4ckE9UXcnLy1u1atX169f7fkWpLVq0yMDAIDo6+rVHSvizAwCQLTwrBJClo0ePpqSklJWVKTCG\nurq6Q4cOHTlypO9XlFpmZmZeXp7oNPEAAH0Ncizol9j7dH1wuTp9ff3/x96dhkVxpf3jv5tFRVEE\nHBYFFwhKAMVRJAlB44bGKKKXCi4oMQrKE8cHMC2LmjwGweSKuNHGy2VIogEFQ0YhmNEgxkTFLTEZ\nIwJGFgEFZJEdge76vzg/69/TCDZQdDfk+3nVdWq7q7r79N2nTp1KTEwMDAxUuONPlXJyciIjI8eO\nHav5K3ZOUVFRREREamoqexwkAIBmQo4FPUxtbW1YWFhhYSERbdy4MT09Xd0RKRo3blxERMSBAwfU\nFcDYsWM7l3yofsVOaG5uPn78eFxcnNqH6gAAaJ+QAzACqIC+vn5kZGRkZKS6A2mPlZXV5s2b1R1F\n76Srqys/0AMAgMZCOxYAAACA8JBjAQAAAAgPORYAAACA8JBjAQAAAAhPsD7v6enpGOLvL449Za8X\nfwz27NnzzTffqDsKAADoGYQZ5/3UqVOnTp3q+nYAiKikpOTOnTvtPxEZQHna2to7d+4cOXKkugMB\ngL8WYXIsAAGp8pk8AAAA3QT9sQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwL\nAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACE\nhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAA\nAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjI\nsQAAAACEhxwLAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAAAACEJ+I4Tt0xAJC7u3teXh57XV9fX15e\nbmlpyc/18/P7xz/+oZ7IAAAAOkVH3QEAEBHl5ubevXtXvqSqqop/XVNTo/KIAAAAugTXCkEjrFq1\nSkenzYzf09NTlcEAAAB0Ha4VgkYoKCgYMWJE60+jSCSaOHHizZs31RIVAABAp6EdCzSCpaXla6+9\npqWl+IHU1tZetWqVWkICAADoCuRYoClWrlwpEokUCmUyGS4UAgBAT4QcCzRF61xKW1t76tSppqam\naokHAACgK5BjgaYYMmTIjBkztLW15QtXrlyprngAAAC6AjkWaBBvb2/5bu9aWloLFixQYzwAAACd\nhhwLNMiCBQt0dXXZax0dnblz5w4ePFi9IQEAAHQOcizQIAMHDnR3d2dpllQq9fb2VndEAAAAnYQc\nCzTLihUrWlpaiEhPT++dd95RdzgAAACdhBwLNMucOXMGDBhARIsXL9bT01N3OAAAAJ2k0ucVNjY2\nnj17ViqVqnKn0ONMmjTp4sWLlpaWp06dUncsoNHMzMwmT56s7igAAF5Mpc/S+fbbbxctWqSy3QFA\n76ajo9Pc3KzuKAAAXkyl7Visnw2ekNjjsNFBExIS1B2I8BISEry8vPCZ7InYe6fuKAAA2oT+WAAA\nAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44F3eW1114T\ni8XqjkJg2dnZu3btio+Pd3R0FIlE9vb29fX1/NzU1NTZs2eLRCInJ6f4+Hg1xhkdHS0SiVSwYlFR\nUUxMjKen5xtvvCFfznHc0aNHx48fr6+v7+joGBMTww9CxnHcsWPH3N3dQ0NDp02b5u/vX1lZSUQt\nLS2bN28uLCzsRNgAAJqIUyH2q6PKPYIglixZsmTJko6u5eXltXXr1u6Ih3n48GHXN9Khz+TFixeX\nLVv27NkzjuOqqqrYN8jX11d+mdzcXCLKzMzsemydduPGDfaoR9WsmJ+fT0RjxoyRLwwODl6xYoVE\nItm4cWO/fv2IaP/+/WzWwYMHiSglJYXjuD/++IOIPDw82KyysrKFCxc+ePBAmf2iPgEADYd2LOgu\nJ0+eDA8P76aN5+bmLl++vJs2/kIZGRmrVq2Kjo7u06cPEQ0aNIiIJk+efOTIEfkmq2HDhhHRqFGj\nVBmbvMrKyjNnzlhaWqpsxeHDhyuUFBQUFBQUfP311++///6+fftOnz5NRPv27WNzjx07RkSTJk0i\nIjs7uyFDhly4cIHNMjY2/uijj+bPn19bW9vRMAAANA1yLOh5CgsL582b9+TJE5XtUSaTeXt7r169\n2tjYWL48Pj7ezMzMz88vJyeHlejq6hIRy8NUj+O48PBwsVjc0QuFnV7xhfLz86OiovjJWbNmDRky\npLS0lE0aGRkR0Y8//khEdXV1FRUV06dP5xd2dHS0trbufVeZAeAvCDkWCE8qlSYkJPj4+EyZMoXj\nuDNnzvj5+VlYWFRWVvr4+BgbGzs4ONy6dYvjuPT09E2bNo0cObK4uHjRokVGRkYODg6JiYlEdOjQ\nIZFIxH7yq6uro6Ki+Mkvv/wyIyOjuLh4/fr1bI9paWkWFhaXLl3qpiNKSkq6ffv222+/rVBubm6e\nkJBQV1e3dOnSpqam1itWVVWJxeKQkJCgoKBZs2YFBQVVVla2c07YWg0NDZ9++umaNWucnJxmzpx5\n584dJeOMjo728vIyMDDo6AF2esUXcnV1NTMzky9pamqaPHkye71nzx4rK6uAgID8/HyJRCIWi+Pi\n4uQXnj179pEjRx48eCBIMAAAaqPKC5PoP9FDdaI/Ft9HRyaTFRQUDBgwgIh27NiRl5d3/PhxInJ2\ndm5paUlOTmaddTZs2HDp0qXY2Fh9fX0iunz5MsdxVlZW8h8Y+Un67w5Ap0+f1tPTS0pK6uihKfmZ\nXLp0KRE1NTXJF/Ir7t69m4g2bdqkUF5dXW1jY/PRRx+xyZKSEhsbm1GjRlVUVLR1TtiSa9euvXfv\nHnvt5uZmYmJSVVX10iCvXr0aFRXFXo8ZM0b571qnV+RRq/5Y8i5fvtyvX79ffvmFLyktLXVxcRk2\nbFhgYGDr5X/99VciioyMbH+nqE8AQMMhx4KX60SOJZPJ5H93R48ezb/1MpnMxMSkT58+bNLGxoaI\namtr2eSePXuIyMvLi2v1ey8/2fpHvbm5ucMHpvRncsSIEQYGBgqF8ke0ePFiet6Pmy8PCwsjokeP\nHvGrfPXVV0QkFou5ts/JtWvXWv8XSk5Obj/CsrKy9957TyqVsknlU6VOryivnRyrubl5ypQpcXFx\n8oV5eXlz585l7YIffPABv3emqKiIiObMmdP+TlGfAICGw7VC6BYK3XrkJ0UikaGhIX9lTUtLi4hY\now4RzZ8/n4ju37/f0T3q6Oh0OtqXKi4uNjQ0bGuuSCSKiYmxtbX18fF59OgRX37lyhUiGjhwIF8y\nZcoUIrp69Sq1fU5u3rxpZ2en8EWdN29e+xH6+/t7e3tnZ2dnZmZmZmY+e/aMiDIzM196xa3TKypp\n+/btM2bMWLZsGV9y/fr1iRMn+vj4nD592sXFZdeuXR9++KH8KoMHDyaikpISQQIAAFCXbvxZAuiE\noUOHElEn7m7rVtra2lKptJ0FBg4cmJiY6OzsvGLFCr6QpY95eXkODg6sxNTUlIja7/ZUXl6em5tb\nV1fH551EJJVKtbW121krKSnp1KlTCoWvvvqqtbX1n3/+2R0rKiM5OXnAgAEhISHyhaGhoeXl5VOn\nTu3bt+/JkyeHDx9++PDhHTt28Auw7JN7Pp4WAEAPhXYs0Czl5eVENHPmTHr+W8taVmQyGRuSiv/p\nbWlpkV9RYVJY5ubmT58+lS9hKZd84mVnZxcTE8Nul2NYq1VKSgpfUlBQQM+Pri22traszztfkpGR\nIZFI2o+wsbFRvt2Lv+T30jyp0yu+1Pnz54uKklHfTwAAIABJREFUiuQTLNaAx5rr2K2XlpaWJiYm\nCq2ebEhSc3PzLgYAAKBeyLGgW9TU1BBRdXU1m2xsbCS59IjNbW5u5pfnM6QLFy5MmDBh3bp1RGRr\na0tEO3bsuH///r59+1iyde7cOalUam1t/fjx44cPH7K1vvvuu8GDB3///ffddDhvvfVWTU0NC5th\nIxEoXM/y9PQMCAjgJzdv3mxvbx8dHf348WNWcuDAARcXlw0bNlDb58TDw2PUqFHh4eFr1qyJjY3d\nunVrQEDA6tWriWjXrl12dnYnTpzoaPzduiIb6V6hnS81NfWTTz5paWmRSCQSiSQ6OjowMPDs2bNE\nxAY2Y6/z8/NLS0vZLQW8srIyInJ1de1otAAAmqUb+3q1gj6qPVRH+7zX1tbyrRdRUVGRkZHsdXh4\n+NOnT1mvdiIKDg6ur69nDSefffbZkydPSkpKdu7cWVNTw7aTlZXl7Ozcv39/Nze3rKwsV1dXb2/v\nEydONDY2hoSEmJmZffPNN2zJ8+fPm5ubX7hwoaOHpuRnkrVOnTt3jk0mJibOmTOHiObOnfvTTz/J\nL9nU1PTmm2/yk9XV1WKx2M3NLSgoSCwWf/zxx6zdiG+XeuE5yc3NdXd3NzQ0NDU19fX1LS0tZVvz\n9/cXiURDhw59acAKXde7b8W0tDRfX18i0tHR+fTTT2/fvs1x3JUrV9h48QrYAO4ymUwikUyaNCko\nKGjBggXbtm1raGiQ3+bnn3+upaX10tHeUZ8AgIYTcSrs9JCQkMDuF1PZHkEQnp6eRJSQkNAdG7e1\ntc3KylLXp0L5z+Q777wzevTovXv3qiCqdmRnZ69cufL69euav2Knubu7m5mZHTlypP3FUJ8AgIbD\ntUIApXzxxRcpKSnFxcVqjKGurm7//v1Hjx7V/BU7LT09PTs7W36YeACAHqpn5FglJSUJCQkRERHq\nDqTb/XWOlFdXV0dEmv98OlNT08TExMDAQBawWuTk5ERGRo4dO1bzV+ycoqKiiIiI1NRU9jhIAIAe\nTUNzLI7j9u/fHxwcPG3atCFDhqxYscLLy4uNha057t696+HhYWxsPGTIkKVLl8oPjNSWzz77bPDg\nwSKRSFtbe9asWfPmzZs7d+6MGTOGDx8uEonOnTv38ccfa+CRdpPa2tqwsLDCwkIi2rhxY3p6uroj\neolx48ZFREQcOHBAXQGMHTu2c8mH6lfshObm5uPHj8fFxWnayB0AAJ2kys5fyvdR3bt374ABA5qb\nmysrKxcuXMieQ9fOwzp4Dx8+bGdSQHfv3l2wYMG3337766+/ent7E9H06dOVWZGNYf3KK6/IF0ql\n0rlz5/75558NDQ2adqRcp8Z57ynQb7rnwnsHABpOQ9uxDh48OGzYMB0dncGDB3/77bdsnKGXys3N\nZbeFv3BSWD/88ENsbOzChQv//ve/x8TEGBgYKNkpmI36ozCepJaWVmhoqL6+Pnt430up8kgBAACg\nEzR0nPeCgoKOXi8oLCycN28eP0iPwqTg/vd//1d+sqWlZc2aNcqsqDDcIvPbb7+9+eabSu5axUcK\nAAAAnaBx7Vjffffd+vXr6+vri4uL169fv379+ta9obOzsxcvXhwcHLxy5crJkyf/5z//IaIvv/wy\nIyODrdV6kojY2Nlr1qxxcnKaOXPmnTt3OI47c+aMn5+fhYVFZWWlj4+PsbGxg4PDrVu3lA9YJpN9\n+OGHe/fu5e/qT0tLs7CwYNc3X6qpqenOnTv/+Mc/XjhXo44UAAAAOkCVFyaV7z9BrfokyZe88sor\nVlZWHMc1NTUZGBjY29u/cC2FybVr1967d4+9dnNzMzExefr0aUFBAXsq3I4dO/Ly8lhnc2dnZyWP\n6Ntvv508eTIRjRw58siRIzKZjOO406dP6+npJSUltXN0CgwMDDT5SNEfCzQQ3jsA0HAaeq2wff7+\n/nyvJmNj46ysrJeucv369aNHjyoM8/Pzzz/Pmzdv2LBh2dnZW7ZsIaLhw4dv2rTpt99+UzKSqVOn\njhkzJi0tbfPmzb6+vjo6Ou+++66Hh0d1dbWOTnvndsyYMZmZmUQklUpzcnIWL16s4Ueanp7ORiLt\nZdgDBHvlofV67L0DANBYPTLHCgoKqq2tPXDgQEVFxbNnz5R5GPDNmzft7Ozu3r3bepZ8BymRSGRo\naMgeRacMQ0NDQ0NDOzs7AwODVatWHTt27N133yWi9hMsedra2jY2Nu+///4L52rOkQIAAECH9Mgc\n68aNG15eXp9//vn7778fGxurzCrl5eW5ubl1dXXsehkjlUoV7u/rNA8PDyLq06dP51b38/N7Ybnm\nHOkbb7zRTc/SUS/2PJZeeWi9Hnvv1B0FAECbNK7PuzJWrVrV3NzMHsork8mIiHveyUmhpYeftLW1\nZT3B+VkZGRn8c3m77vHjx0T0zjvvvDAMeVxHHq+mgUcKAAAAytDEdqyKigoiampq4kvq6+uJqLGx\nkU0+fvy4urr6hx9+KC0tffr0KRHduHFj6NCh1tbWjx8/fvjw4fDhw4lIftLDw2PUqFHh4eFFRUXT\np0+/d+/ejRs3vvnmG36zHMexS2k1NTVE1NzcrKur206Qu3fvNjAwWLRo0eDBgxsbG4ODgz09PTds\n2EBE33333dKlS0+dOsVyIwXsSSzsiFrTwCMFAACATtC4dqw7d+6EhYURUV5e3vbt23///fecnJyQ\nkBAiys/P37t3b2VlZWRk5KBBg7Zs2WJtbb1ly5bBgwdHRkb2799/yZIlgwYNunnzJtuU/GS/fv3S\n0tLc3d3/9a9/bdq0qbS0NDY2dtCgQQcOHMjPzyeiiIiIqqqqvXv3skfibNu2jQ253pbq6uqdO3eO\nGjXK398/ODh4w4YNJ0+e1NLSIqK+ffsOGjSob9++rde6evUqG1iroKAgJCTk119/lZ+rmUcKAAAA\nnSDq0KWrLmL9J1S5RxAEu+2uV3Zawmey58J7BwAaTuPasTSHqG1s2AUAAACAtmhifywNgf/HAAAA\n0GloxwLoLtnZ2bt27YqPj3d0dBSJRPb29vL3OqSmps6ePVskEjk5ObEhy1WJ47ijR4+OHz9eX1/f\n0dExJiaG/1PBcdw///nPJUuWbNmyZe3atXFxcS/cQnR0tPyIaxzHHTt2zN3dPTQ0dNq0af7+/pWV\nlUTU0tKyefPmwsJCFRwUAIBmUeWg8nj2RQ/V3c/Sefjwobo20n2fyYsXLy5btuzZs2ccx1VVVbGv\nm6+vr/wyubm5RJSZmdkdAbQvODh4xYoVEolk48aN/fr1I6L9+/ezWdu3bx8xYkRFRQXHcRUVFSNG\njNi7d6/C6jdu3NDT05M/dQcPHiSilJQUjuP++OMPIvLw8GCzysrKFi5c+ODBA2EPAfUJAGg4tGOB\nmuXm5i5fvlwTNiKgjIyMVatWRUdHs2FpBw0aRESTJ08+cuSIfJPVsGHDiGjUqFEqDq+goKCgoODr\nr79+//339+3bd/r0aSLat28fEeXn54eHh69bt87Q0JCIDA0NfX19w8LCysvL+dUrKyvPnDljaWkp\nv81jx44R0aRJk4jIzs5uyJAhFy5cYLOMjY0/+uij+fPnt36+OwBAL4YcC9SpsLBw3rx5T548UftG\nBCSTyby9vVevXm1sbCxfHh8fb2Zm5ufnl5OTw0rYyGSdfjxAp+Xn50dFRfGTs2bNGjJkCHuwUmxs\nbEtLy4wZM/i506dPr6+v55+AyXFceHi4WCyWv1BIREZGRkT0448/ElFdXV1FRcX06dP5uY6OjtbW\n1mKxuBuPCgBAwyDHAsFUVVWJxeKQkJCgoKBZs2YFBQWxHjmHDh1i92MSUXV1dVRUFD/55ZdfZmRk\nFBcXr1+/nuO49PT0TZs2jRw5sri4eNGiRUZGRg4ODomJicpvhEWSlpZmYWFx6dIltZyHpKSk27dv\nv/322wrl5ubmCQkJdXV1S5culR9il/fCE8hx3JkzZ/z8/CwsLCorK318fIyNjR0cHG7dusXWYsP6\nr1mzxsnJaebMmXfu3HlphK6urmZmZvIlTU1NkydPJqLLly8TkYWFBT+LtVf9/vvvbDI6OtrLy8vA\nwEBhm3v27LGysgoICMjPz5dIJGKxWKEj1+zZs48cOfLgwYOXhgcA0Euo8sIk+k/0UMr0x6qurrax\nsfnoo4/YZElJiY2NzahRo1iWYGVlJf/Wy08S0ZgxYziOa2lpSU5OZn2DNmzYcOnSpdjYWH19fSK6\nfPmykhthTp8+raenl5SU9NJD647P5NKlS4moqalJvpDfy+7du4lo06ZNCuVtncCKioqCggL29Mkd\nO3bk5eUdP36ciJydndmSa9euvXfvHnvt5uZmYmJSVVXVoYAvX77cr1+/X375heM4R0dHIqqvr+fn\nsicTvP766xzHXb16NSoqipWPGTNG4dSVlpa6uLgMGzYsMDCw9V7YiLuRkZEdiq0dqE8AQMMhx4KX\nUybHYqPzP3r0iC/56quviEgsFnOtfo/lJxXSIxsbGyKqra1lk3v27CEiNtSk8hvhOK65uVmZQ+uO\nz+SIESMMDAwUCvm9yGSyxYsX0/Pu4Xx5+ydw9OjR8lswMTHp06cPx3HXrl1r/ccpOTlZ+Wibm5un\nTJkSFxfHJllrVkNDA78AuxdywoQJZWVl7733nlQqZeWtc6y8vLy5c+eyBrwPPviAX5IpKioiojlz\n5igfW/tQnwCAhsO1QhDGlStXiGjgwIF8yZQpU4jo6tWrHdoOex4Ra7Yhovnz5xPR/fv3OxqPjo7a\nxn4rLi5mHcZfSCQSxcTE2Nra+vj4sMcZMe2fQPnOTyKRyNDQkF1tvHnzpp2dncK3et68ecpHu337\n9hkzZixbtoxN2traEhF7OCbDLvgOHTrU39/f29s7Ozs7MzMzMzPz2bNnRJSZmcku/12/fn3ixIk+\nPj6nT592cXHZtWvXhx9+KL+jwYMHE1FJSYnysQEA9GjIsUAYLDfKy8vjS0xNTYmodcedDhk6dCg9\n7xLUU2hra0ul0nYWGDhwYGJiYkNDw4oVK/jCzp3A8vLy3NxcdjmP1/7e5SUnJw8YMEA+GbK3tyci\n+eTv8ePHROTq6pqUlDR9+vRXn2Ohvvrqq7Nnzyai0NDQ8vLyqVOn9u3b9+TJk0R0+PBh+X2xNJHD\n0L4A8JeBHAuEwRpdUlJS+JKCggIimjlzJj3/fWUtHzKZjI0Xxf/ctrS0tLVZNmRAJzbSzja7m7m5\nuXw7ED1PeuRTHzs7u5iYGHYXHtP+CWyLra0t6/POl2RkZEgkEmXiPH/+fFFREXsMOXP16tWVK1ca\nGBhcvHiRL0xLS9PV1V2+fHljY6N8axl/rfDPP/8kItauxu6RtLS0NDExUbjxkLWHmZubKxMbAEAv\ngBwLhLF582Z7e/vo6GjW7EFEBw4ccHFx2bBhAz2/ArVjx4779+/v27eP5Unnzp2TSqXW1taPHz9+\n+PCh/Nb4DOnChQsTJkxYt25dhzby3XffDR48+Pvvv1fNsSt46623ampqampq+BI2LILCZTJPT8+A\ngAB+sv0T2NjYSHIJJdt4c3Ozh4fHqFGjwsPD16xZExsbu3Xr1oCAgNWrVxPRrl277OzsTpw48cIg\nU1NTP/nkk5aWFolEIpFIoqOjAwMDz549a2RkFBoaeujQIbaL6urqw4cPb9269aVNiWx8srNnzxJR\nfn5+aWkp6/vPKysrIyJXV9eXnT8AgN5CNd2+GPRR7aGUHOe9urpaLBa7ubkFBQWJxeKPP/6Yb/nI\nyspydnbu37+/m5tbVlaWq6urt7f3iRMnGhsbQ0JCzMzMvvnmG7Ykax357LPPnjx5UlJSsnPnzpqa\nmo5u5Pz58+bm5hcuXHhpzN3xmWStU+fOnWOTiYmJc+bMIaK5c+f+9NNP8ks2NTW9+eab/GRbJ5Bv\nlwoPD3/69Cm7D4CIgoOD6+vrc3Nz3d3dDQ0NTU1NfX19S0tL2db8/f1FItHQoUNbR3jlyhU2SrsC\nNhS7TCY7evSot7d3WFjY4sWLDx8+LJPJWm9Eoc+7TCaTSCSTJk0KCgpasGDBtm3b5DvOcxz3+eef\na2lpCTjaO+oTANBwIk6F3SMSEhLYDWIq2yMIwtPTk4gSEhJUsC9bW9usrCyVfUi66TP5zjvvjB49\neu/evcJutqOys7NXrlx5/fp19YbBuLu7m5mZHTlyRKgNoj4BAA2Ha4UAwvviiy9SUlKKi4vVGENd\nXd3+/fv58dnVKz09PTs7W35weQCAXg85FmgWdotcT3+wnampaWJiYmBgoMIdf6qUk5MTGRk5duxY\ndQXAKyoqioiISE1NZc9tBAD4i0COBZqitrY2LCyssLCQiDZu3Jienq7uiLpk3LhxERERBw4cUFcA\nY8eO1YScprm5+fjx43FxcT1rAA4AgK5T2ziNAAr09fUjIyMjIyPVHYhgrKysNm/erO4o1ExXV1d+\neAgAgL8OtGMBAAAACA85FgAAAIDwkGMBAAAACA85FgAAAIDwkGMBAAAACE+l9xXq6OjQ8yf7Qo/T\ni9+4XnxovRurUgAANJNKn6XT2Nh49uxZqVSqsj1CT5Senr5nzx7VPLoHejQzM7PJkyerOwoAgBdT\naY4FoAw8hw4AAHoB9McCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADh\nIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAA\nAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5y\nLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAAEB5yLAAAAADhIccCAAAA\nEB5yLAAAAADhIccCAAAAEB5yLAAAAADh6ag7AAAiorKysurqava6pKSEiHJycvi55ubmenp66okM\nAACgU0Qcx6k7BgAyMjKqrKxsa+769esPHjyoyngAAAC6CNcKQSO4uLhoabX5aXRxcVFlMAAAAF2H\nHAs0gre3d1tNqn379l24cKGK4wEAAOgi5FigEebPn9+vX7/W5To6OvPnz9fX11d9SAAAAF2BHAs0\nQv/+/RcsWKCrq6tQLpVKV6xYoZaQAAAAugI5FmiK5cuXNzc3KxQOGDDg7bffVks8AAAAXYEcCzTF\n7NmzBw0aJF+iq6vr5eXVt29fdYUEAADQacixQFPo6uouW7asT58+fElzc/Py5cvVGBIAAECnYXws\n0CCXLl2aOnUqPzlkyJDi4mJtbW31RQQAANBJaMcCDTJ58mRTU1P2WldXd+XKlUiwAACgh0KOBRpE\nS0vL29ubXS5sbm5etmyZuiMCAADoJFwrBM3yyy+/ODk5EZGlpWV+fr5IJFJ3RAAAAJ2BdizQLBMn\nTrS2tiaid999FwkWAAD0XDrqDkBZeXl5oaGhUqlU3YFAt2ODNdy4ccPT01PdsUC3s7Cw2L17t7qj\nAAAQXo9px7px48bJkyfVHQUoKigoOHXqlLDbfOWVVyZOnKgwVpZanDp1qqCgQN1R9GYFBQV79uxR\ndxQAAN2ix/THSkhI8PLy6inR/nX07vdFJBLFx8ejOa379O7PDwD8xfWYdiwAAACAHgQ5FgAAAIDw\nkGMBAAAACA85FgAAAIDwkGMBAAAACA85FgAAAIDwkGOBerz22mtisVjdUQgpOzt7165d8fHxjo6O\nIpHI3t6+vr6en5uamjp79myRSOTk5BQfH6/i2DiOO3r06Pjx4/X19R0dHWNiYvjhEjiO++c//7lk\nyZItW7asXbs2Li7uhVuIjo6WH3af47hjx465u7uHhoZOmzbN39+/srKSiFpaWjZv3lxYWKiCgwIA\n0HRcD8F+ltQdBSjq9Pvi5eW1detWwePhPXz4sOsbIaL4+Hhllrx48eKyZcuePXvGcVxVVRX7cvn6\n+sovk5ubS0SZmZldD6yjgoODV6xYIZFINm7c2K9fPyLav38/m7V9+/YRI0ZUVFRwHFdRUTFixIi9\ne/cqrH7jxg09PT35N/rgwYNElJKSwnHcH3/8QUQeHh5sVllZ2cKFCx88eKBMYPheA0Av1mNqN9TF\nmkkz35ecnBxXV9eub0fJHOvu3buWlpZlZWXyK06ePJmITp48yRc2NTUREcvDVOnhw4fLly/nJ//9\n738TkbW1NcdxeXl5Ojo6kZGR/NwdO3b0799f/lgqKiq2bNkyevRo+Tf6jTfeIKLS0lKO42Qy2ZAh\nQ/T19fm5v/32m729fU1NzUtj08zPDwCAIHCtEHqbwsLCefPmPXnyRDW7k8lk3t7eq1evNjY2li+P\nj483MzPz8/PLyclhJbq6ukTUp08f1QTGy8/Pj4qK4idnzZo1ZMiQ0tJSIoqNjW1paZkxYwY/d/r0\n6fX19UePHmWTHMeFh4eLxWKF53MbGRkR0Y8//khEdXV1FRUV06dP5+c6OjpaW1v3smvBAAAdhRwL\nVE0qlSYkJPj4+EyZMoXjuDNnzvj5+VlYWFRWVvr4+BgbGzs4ONy6dYvjuPT09E2bNo0cObK4uHjR\nokVGRkYODg6JiYlEdOjQIZFIxH74q6uro6Ki+Mkvv/wyIyOjuLh4/fr1bI9paWkWFhaXLl3qjsNJ\nSkq6ffv222+/rVBubm6ekJBQV1e3dOlS1oKloKqqSiwWh4SEBAUFzZo1KygoqLKysp0TwtZqaGj4\n9NNP16xZ4+TkNHPmzDt37rw0QldXVzMzM/mSpqYm1sx2+fJlIrKwsOBnWVpaEtHvv//OJqOjo728\nvAwMDBS2uWfPHisrq4CAgPz8fIlEIhaLFTpyzZ49+8iRIw8ePHhpeAAAvZZ6m9GUh2sKmqlz70t+\nfj4RjRkzRiaTFRQUDBgwgIh27NiRl5d3/PhxInJ2dm5paUlOTmadhzZs2HDp0qXY2Fh9fX0iunz5\nMsdxVlZW8ruWn2Qb52edPn1aT08vKSmpo3GSEtcKly5dSkRNTU0KK7IXu3fvJqJNmzYplFdXV9vY\n2Hz00UdssqSkxMbGZtSoURUVFW2dELbk2rVr7927x167ubmZmJhUVVV16KAuX77cr1+/X375heM4\nR0dHIqqvr+fn1tXVEdHrr7/OcdzVq1ejoqJY+ZgxYxTe6NLSUhcXl2HDhgUGBrbey6+//kpE8lch\nXwjfawDoxXpM7Ya6WDN17n2RyWTyaZB8Xx+ZTGZiYtKnTx82aWNjQ0S1tbVscs+ePUTEniKs8Ksv\nP6mQY3Ec19zc3NEgOeVyrBEjRhgYGLRekT+cxYsX0/Pu4Xx5WFgYET169Ihf5auvviIisVjMtX1C\nrl271vpvUnJysvJH1NzcPGXKlLi4ODbJWrMaGhr4Bdi9kBMmTCgrK3vvvfekUikrb51j5eXlzZ07\nlzXgffDBB/ySTFFRERHNmTOn/XjwvQaAXgzXCkENFDr3yE+KRCJDQ0P+4pqWlhYRsXYdIpo/fz4R\n3b9/v6N71NHR6XS07SsuLjY0NGxrrkgkiomJsbW19fHxefToEV9+5coVIho4cCBfMmXKFCK6evUq\ntX1Cbt68aWdnp/AdnjdvnvLRbt++fcaMGcuWLWOTtra2RPT06VN+ATYEw9ChQ/39/b29vbOzszMz\nMzMzM589e0ZEmZmZ7PLf9evXJ06c6OPjc/r0aRcXl127dn344YfyOxo8eDARlZSUKB8bAEAvgxwL\nepKhQ4fS8z5DGkJbW1sqlbazwMCBAxMTExsaGlasWMEXstwxLy+PLzE1NSWi1j2f5JWXl+fm5rLL\nebz29y4vOTl5wIAB8smQvb09Ecknf48fPyYiV1fXpKSk6dOnv/ocC/XVV1+dPXs2EYWGhpaXl0+d\nOrVv374nT54kosOHD8vvi6WJ3PNRuAAA/oKQY0FPUl5eTkQzZ86k57/irH1FJpOxUan4H/WWlhb5\nFRUmBWRubi7fDkTPkx751MfOzi4mJobdhcewVquUlBS+pKCggJ4fWltsbW1Zn3e+JCMjQyKRKBPn\n+fPni4qKQkJC+JKrV6+uXLnSwMDg4sWLfGFaWpquru7y5csbGxvlW8v4a4V//vknEbF2NXaPpKWl\npYmJiULbJGsPMzc3VyY2AIBeCTkWqEFNTQ0RVVdXs8nGxkaSS4/Y3ObmZn55PkO6cOHChAkT1q1b\nR8+vc+3YseP+/fv79u1jyda5c+ekUqm1tfXjx48fPnzI1vruu+8GDx78/fffd8exvPXWWzU1NSxm\nhg2LoHCZzNPTMyAggJ/cvHmzvb19dHQ0azciogMHDri4uGzYsIHaPiEeHh6jRo0KDw9fs2ZNbGzs\n1q1bAwICVq9eTUS7du2ys7M7ceLEC4NMTU395JNPWlpaJBKJRCKJjo4ODAw8e/askZFRaGjooUOH\n+Hfk8OHDW7dufWlL4fLly4no7NmzRJSfn19aWsr6/vPKysqIyNXV9WXnDwCg91JNt6+uQ99YzdSJ\n96W2tpZvTYmKioqMjGSvw8PDnz59ynq1E1FwcHB9fT1rPvnss8+ePHlSUlKyc+dOfmTLrKwsZ2fn\n/v37u7m5ZWVlubq6ent7nzhxorGxMSQkxMzM7JtvvmFLnj9/3tzc/MKFCx09OlKizztrnTp37hyb\nTExMnDNnDhHNnTv3p59+kl+yqanpzTff5Cerq6vFYrGbm1tQUJBYLP74449Z0xHfLvXCE5Kbm+vu\n7m5oaGhqaurr68tGAeU4zt/fXyQSDR06tHWEV65cYaO0K2BDsctksqNHj3p7e4eFhS1evPjw4cMy\nmaz1RhT6vMtkMolEMmnSpKCgoAULFmzbtk2+4zzHcZ9//rmWltZLR3vH9xoAejER10M6TCQkJLC7\nydQdCPyX7n5fbG1ts7Ky1PW+i0Si+Ph4T0/P9hd75513Ro8evXfvXtVE1Zbs7OyVK1dev35dvWEw\n7u7uZmZmR44caX8xfK8BoBfDtUKArvoRYM5sAAAgAElEQVTiiy9SUlKKi4vVGENdXd3+/fv58dnV\nKz09PTs7W35weQCAvyDkWKDR2D10tbW16g6kPaampomJiYGBgQp3/KlSTk5OZGTk2LFj1RUAr6io\nKCIiIjU1ddCgQeqOBQBAnXp/jqVwzxf0FLW1tWFhYYWFhUS0cePG9PR0dUfUnnHjxkVERBw4cEBd\nAYwdO1YTcprm5ubjx4/HxcVp1PgaAABq0WtzrMbGxoiIiDfeeEPhSb1qFx0drXCXe1t++OGHOXPm\nsMfwTZs2bdq0aU5OTvPnzz969Ci7h65309fX55/EEhMT88Ybb6g7opewsrLavHmzuqNQM11d3ZCQ\nEE3I9gAA1K7X5lj9+vULCgrKyspij23REDdv3gwODlZyYTc3N9a9ZuTIkRcvXrx48eKNGzfWrl27\nc+dOBweHu3fvdmekAAAA0CW9NsciIj09PRMTE3VH8f+rrKw8c+ZMh66hDBs2jIj69u3LJrW0tObP\nn3/58uXa2loPD4+GhoZuCRQAAAC6rDfnWBqF47jw8HCxWKzkhcJ2mJubh4eHP3jwAPdtAQAAaKze\nlmPV19cHBQX5+flt3bo1NDRU/j4v9hCSNWvWODk5zZw5886dOxzHnTlzxs/Pz8LCorKy0sfHx9jY\n2MHB4datW2yVmzdvvvbaa++///62bdt0dHTYWNitt6NMYNHR0V5eXq2fRpeWlmZhYXHp0qUOHebi\nxYu1tLTOnz+vCYcGAAAAL6CeoU87TpnxoJubm52dndeuXcsGqv7zzz+1tbX5tdauXXvv3j322s3N\nzcTE5OnTpwUFBQMGDCCiHTt25OXlHT9+nIicnZ3ZYjY2NoaGhmxrnp6eJSUlL9xOVVVV+4FdvXo1\nKiqKvVYYL/v06dN6enpJSUltrUtEY8aMaV1uZmZmZGSk9kPr3eN0kxLjvENX9O7PDwD8xfWY2k2Z\nujg6OpqI7t69y5fY2Niwta5du9Y6v0xOTuY4bvTo0fyWZTKZiYlJnz592OSQIUOIaO/evVKp9M6d\nO1VVVe1spy1lZWXvvfeeVCplkwo5Fsdxzc3N7azeVo5lYWFhbm6u3kPjnr8vAF3R/mcMAKCH0lF3\n7Sokdu1s1KhRfImW1v+7GHrz5k07O7sX3osn30FKJBIZGhqyZ/oS0cGDB1evXh0QEHD8+HGJRDJo\n0KB2ttMWf39/f3//7OxsNsmGXcjMzNTV1bW2tiYiHZ0OvwtNTU0lJSUzZ85U76HxEhISOrGW5vP0\n9AwMDNT8YSN6rvT0dP6BjAAAvUyvyrGKioqIqLy83MLCQmFWeXl5bm5uXV0du3zGSKVSdjGxLYsX\nL/773//+P//zP+fPn588efKRI0c6sZ2kpKRTp04pFL766qvW1tZ//vmn8kcnLy0trbm5ecaMGeo9\nNN6SJUs6dyCa7/XXX+/FR6d2HJ5UCAC9V6/q825ra0tEKSkpL5zFOnTzJRkZGRKJpP0Nfvjhh9bW\n1ufOnYuLi2tpadm6dWsnttPY2CjfcshfK+QTrJaWFqUPkYjo2bNnYWFh48eP37hxo3oPDQAAANqk\nnkuUHadMf6zbt29ra2sbGRl9//339fX1Fy5cGDhwIBHl5OQ0NDSwa4jvvffe119/vWXLFjc3N9ah\ne8SIEUTEen9zHDd06FAiampq4jhOT0+voqKC47impqZBgwY5Ozu3sx0lKfTHSk5OHjBgwNmzZ1+4\nMLsvcsSIEXzJL7/8Mnny5JEjR/LdztR7aL27zzKhz3s3692fHwD4i+tV7Vjjx49PS0uztbVdsmSJ\ng4PDjRs3xo8fv27dupycHF1d3bS0NHd393/961+bNm0qLS2NjY0dNGjQgQMH8vPziSgiIqKqqmrv\n3r2PHj0iom3btjU0NDQ0NMyYMeOTTz559913J0+efPLkyX79+r1wO52OuW/fvoMGDeJHGZV3+fLl\nf/zjH0SUn58/derU2bNnz58/PyIiwsvL686dO3Z2dmyxtkJS+6EBAAD8lYm4HtIfIiEhwcvLq6dE\n+9fRu98XkUgUHx/v6emp7kB6rd79+QGAv7he1Y6lRqK2ZWZmqjs6AAAAULVedV+hGuGPOAAAAMhD\nOxaA8LKzs3ft2hUfH+/o6CgSiezt7evr6/m5qamps2fPFolETk5OahnEtaioKCYmxtPTU2HoL47j\njh49On78eH19fUdHx5iYGP7PA8dxx44dc3d3Dw0NnTZtmr+/f2VlJRG1tLRs3ry5sLBQ9UcBAKDp\n1NfdvmNw/5Fm6u735eHDh2rcCHXqvsKLFy8uW7bs2bNnHMdVVVWxL5qvr6/8Mrm5uUSUmZnZucC6\njt0PofAIgeDg4BUrVkgkko0bN/br14+I9u/fz2YdPHiQiFJSUjiO++OPP4jIw8ODzSorK1u4cOGD\nBw86EQa+1wDQi6EdCzRXbm7u8uXLNWEjysvIyFi1alV0dHSfPn2IiN2YyUZ5lW+yGjZsGP33MwlU\nbPjw4QolBQUFBQUFX3/99fvvv79v377Tp08T0b59+9jcY8eOEdGkSZOIyM7ObsiQIRcuXGCzjI2N\nP/roo/nz59fW1qruAAAANB5yLNBQhYWF8+bNe/Lkido3ojyZTObt7b169WpjY2P58vj4eDMzMz8/\nv5ycHFaiq6tLRCwP0xD5+flRUVH85KxZs4YMGcI/fMnIyIiIfvzxRyKqq6urqKiYPn06v7Cjo6O1\ntbVYLFZpxAAAmg05FqhCVVWVWCwOCQkJCgqaNWtWUFAQ681z6NAhdvclEVVXV0dFRfGTX375ZUZG\nRnFx8fr16zmOS09P37Rp08iRI4uLixctWmRkZOTg4JCYmKj8RlgkaWlpFhYWly5d6o7DTEpKun37\n9ttvv61Qbm5unpCQUFdXt3Tp0qamJiXPD8dxZ86c8fPzs7CwqKys9PHxMTY2dnBwuHXrFluLjcu/\nZs0aJyenmTNn3rlzpyvBu7q6mpmZyZc0NTVNnjyZvd6zZ4+VlVVAQEB+fr5EIhGLxXFxcfILz549\n+8iRIw8ePOhKDAAAvYq6L1YqC/02NJMy70t1dbWNjc1HH33EJktKSmxsbEaNGsXSCCsrK/ktyE/S\n8w5DLS0tycnJrIfQhg0bLl26FBsbq6+vT0SXL19WciPM6dOn9fT0kpKSlDk66mB/rKVLl9LzkfTl\nN8Je7N69m4g2bdqkUN7W+amoqCgoKGCPj9yxY0deXt7x48eJyNnZmS25du3ae/fusddubm4mJiYd\neuQAteqPJe/y5cv9+vX75Zdf+JLS0lIXF5dhw4YFBga2Xv7XX38losjISOUD4PC9BoBercfUbqiL\nNZMy70tYWBgRPXr0iC/56quviEgsFnOtHi4kP6mQBNjY2BBRbW0tm9yzZw8RsREsld8Ix3HNzc1K\nHl1Hc6wRI0YYGBi03gh7IZPJFi9eTM97jvPl7Z+f0aNHy2/BxMSkT58+HMddu3at9V+m5ORk5aNt\nJ8dqbm6eMmVKXFycfGFeXt7cuXNZK90HH3wglUrl57Inss+ZM0f5ADh8rwGgV8O1Quh2V65cISL2\n7EhmypQpRHT16tUObUdLS4uIWLsOEc2fP5+I7t+/39F4dHS6a1i44uJiQ0PDtuaKRKKYmBhbW1sf\nHx/2XCOm/fPDLnryWzA0NGRXG2/evGlnZ6fwfZ43b54gB7J9+/YZM2YsW7aML7l+/frEiRN9fHxO\nnz7t4uKya9euDz/8UH6VwYMHE1FJSYkgAQAA9ALIsaDbsdwoLy+PLzE1NSUiAwODrmyWPeLa0tKy\nS8EJSltbWyqVtrPAwIEDExMTGxoaVqxYwRd27vyUl5fn5uayp4bz2t+7kthzyhVSqNDQ0PLy8qlT\np/bt2/fkyZNEdPjwYfkFWC7IYTBeAIDnkGNBt2OtMikpKXxJQUEBEc2cOZOe/zY/e/aMiGQyGRtQ\niv+pbmlpaWuz5eXlndtIO9vsInNz86dPn8qXsKRHPvWxs7OLiYlhN+gx7Z+fttja2rI+73xJRkaG\nRCLp4iGcP3++qKgoJCSEL2HNaazxjN0IaWlpaWJiIt/ARkTsJgZzc/MuBgAA0Gsgx4Jut3nzZnt7\n++jo6MePH7OSAwcOuLi4bNiwgYhsbW2JaMeOHffv39+3bx/Lk86dOyeVSq2trR8/fvzw4UP5rfEZ\n0oULFyZMmLBu3boObeS7774bPHjw999/3x1H+tZbb9XU1NTU1PAlbOwDhStonp6eAQEB/GT756ex\nsZHk8kW28ebmZg8Pj1GjRoWHh69ZsyY2Nnbr1q0BAQGrV68mol27dtnZ2Z04caKdUNm48wrtXqmp\nqZ988klLS4tEIpFIJNHR0YGBgWfPniUiNsYYe52fn19aWso6+PPKysqIyNXVtQPnCwCgd1N5D7BO\nQt9YzaTk+1JdXS0Wi93c3IKCgsRi8ccff9zY2MhmZWVlOTs79+/f383NLSsry9XV1dvb+8SJE42N\njSEhIWZmZt988w1bkvVk/+yzz548eVJSUrJz586ampqObuT8+fPm5uYXLlxQ5uiog33eWevUuXPn\n2GRiYuKcOXOIaO7cuT/99JP8kk1NTW+++eZLzw/fLhUeHv706VPWzZ+IgoOD6+vrc3Nz3d3dDQ0N\nTU1NfX19S0tL2db8/f1FItHQoUPbijMtLc3X15eIdHR0Pv3009u3b3Mcd+XKFT09vdZVBBvAXSaT\nSSSSSZMmBQUFLViwYNu2bQ0NDfLb/Pzzz7W0tDo62ju+1wDQi4m4HtJ/IiEhgd1Bpu5A4L+o8n2x\ntbXNyspS5WdAJBLFx8d7enoqv8o777wzevTovXv3dl9UysjOzl65cuX169dVtkd3d3czM7MjR450\naC18rwGgF8O1QgAhffHFFykpKcXFxWqMoa6ubv/+/UePHlXZHtPT07Ozs+WHiQcAAORY0GOwe+g0\n/KF4pqamiYmJgYGBCnf8qVJOTk5kZOTYsWNVs7uioqKIiIjU1FT2cEYAAGCQY0EPUFtbGxYWVlhY\nSEQbN25MT09Xd0TtGTduXERExIEDB9QVwNixY1WW7jQ3Nx8/fjwuLk6jBtEAANAE3TUYI4CA9PX1\nIyMjIyMj1R2IsqysrDZv3qzuKFRBV1dXfqAHAADgoR0LAAAAQHjIsQAAAACEhxwLAAAAQHjIsQAA\nAACE18P6vJ86dUrdIcB/uXbtGvXq9+XatWsKD+YDAbHPDwBAr9Rjxnn/+eefp0+f3n1P8wUAtbCw\nsGDPwAYA6GV6TI4Ffx14vgoAAPQC6I8FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAA\nIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44F\nAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADC\nQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAA\nAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDzkWAAAAADCQ44FAAAAIDwddQcAQER0/PjxR48esdf/\n+c9/iOjTTz/l506bNs3Z2Vk9kQEAAHSKiOM4dccAQH/7298qKip0dXVbz3r27NmGDRuio6NVHxUA\nAECn4VohaAQvLy9tbe1nL0JES5YsUXeAAAAAHYN2LNAIV65ccXV1feGsv/3tb8XFxVpa+D8AAAA9\nCX63QCO4uLhYWFi0Lu/Tp8+7776LBAsAAHoc/HSBRhCJRN7e3q37YzU1NS1btkwtIQEAAHQFrhWC\npvjPf/7j6OioUGhlZfXgwQO1xAMAANAVaMcCTTFu3LgxY8bIl/Tp08fHx0dd8QAAAHQFcizQICtX\nrpS/XNjU1LR06VI1xgMAANBpuFYIGiQnJ+eVV15hn0mRSDRu3LjffvtN3UEBAAB0BtqxQINYWVlN\nmDBBJBIRkba2Ni4UAgBAz4UcCzTLqlWrtLW1iUgqlXp6eqo7HAAAgE7CtULQLMXFxcOGDeM47s03\n3/z555/VHQ4AAEAnoR0LNIuZmdlbb73FcRwuFAIAQM/GabyffvpJR0dH3ecJALqRhYWFINVFYGCg\nug8FAP66AgMD5WukHpC7PH78uKWlJSEhQd2BQIelp6fv2bOno+8dx3GVlZVGRkbdFJVQPD09AwMD\n33jjDXUH0uOxz4kgmyosLHz99deDgoIE2RrAS3WuluspUMt1yO7duwsLC+VLekCOxSxZskTdIUCH\ncRxHvfq9e/3113vx0akMJ2ivUEtLS7wpoDKo5YB36tQphRL0xwIAAAAQHnIsAAAAAOEhxwIAAAAQ\nHnIsAAAAAOEhxwIAAAAQHnIsAAAAAOEhxwJN9Nprr4nFYnVHIaTs7Oxdu3bFx8c7OjqKRCJ7e/v6\n+np+bmpq6uzZs0UikZOTU3x8vOrDKyoqiomJ8fT0VBgIh+O4o0ePjh8/Xl9f39HRMSYmhh9ngeO4\nY8eOubu7h4aGTps2zd/fv7KykohaWlo2b96sMEgMAChALadi7dRy//znP5csWbJly5a1a9fGxcWx\ncmGqMkHGVu5W7M1QdxTQGZ1+77y8vLZu3Sp4PLyHDx92fSNEFB8fr8ySFy9eXLZs2bNnzziOq6qq\nYl89X19f+WVyc3OJKDMzs+uBdU5+fj4RjRkzRr4wODh4xYoVEolk48aN/fr1I6L9+/ezWQcPHiSi\nlJQUjuP++OMPIvLw8GCzysrKFi5c+ODBAyV3LeB3fMmSJUuWLBFkUwDKQC3H9Nxabvv27SNGjKio\nqOA4rqKiYsSIEXv37mWzOlqVta5/ekDughyr59LM9y4nJ8fV1bXr21Gy9rl7966lpWVZWZn8ipMn\nTyaikydP8oVNTU1ExGoodVGofR4+fLh8+XJ+8t///jcRWVtbs0n2X7C0tJTjOJlMNmTIEH19fX7h\n3377zd7evqamRpn9IseCngu1HNeTa7m8vDwdHZ3IyEi+ZMeOHf379+ePpUNVWev6B9cK4a+lsLBw\n3rx5T548Uc3uZDKZt7f36tWrjY2N5cvj4+PNzMz8/PxycnJYia6uLhH16dNHNYEpIz8/Pyoqip+c\nNWvWkCFDSktL2SR72NGPP/5IRHV1dRUVFdOnT+cXdnR0tLa27mWXQgB6BNRyyouNjW1paZkxYwZf\nMn369Pr6+qNHj7LJLlZlyLFAs0il0oSEBB8fnylTpnAcd+bMGT8/PwsLi8rKSh8fH2NjYwcHh1u3\nbnEcl56evmnTppEjRxYXFy9atMjIyMjBwSExMZGIDh06JBKJRCIREVVXV0dFRfGTX375ZUZGRnFx\n8fr169ke09LSLCwsLl261B2Hk5SUdPv27bfffluh3NzcPCEhoa6ubunSpey/nYKqqiqxWBwSEhIU\nFDRr1qygoKDKysp2Tghbq6Gh4dNPP12zZo2Tk9PMmTPv3LnTleBdXV3NzMzkS5qamth/UyLas2eP\nlZVVQEBAfn6+RCIRi8V8PwZm9uzZR44cefDgQVdiAOh9UMsxmlDLXb58mYgsLCz4EktLSyL6/fff\n+ZIuVWVda3VTBc1siQVldO694y+Zy2SygoKCAQMGENGOHTvy8vKOHz9ORM7Ozi0tLcnJyayH0IYN\nGy5duhQbG6uvr8++MxzHWVlZye9afpL+u6349OnTenp6SUlJHY2TlGhFX7p0KRE1NTUprMhe7N69\nm4g2bdqkUF5dXW1jY/PRRx+xyZKSEhsbm1GjRlVUVLR1QtiSa9euvXfvHnvt5uZmYmJSVVXVoSNS\n6Kkg7/Lly/369fvll1/4ktLSUhcXl2HDhik8ap759ddfiUi+Eb4tuFYIPRdquR5dyzk6OhJRfX09\nX1JXV0dEr7/+Ol+ifFWG/ligUp1772QymfzXYPTo0fxGZDKZiYlJnz592KSNjQ0R1dbWssk9e/YQ\nkZeXF8dxY8aMkd+1/GTrTKK5ubmjQXLK1T4jRowwMDBovSJ/OIsXL6bnPcf58rCwMCJ69OgRv8pX\nX31FRGKxmGv7hFy7dq31n6jk5OQOHVFbOVZzc/OUKVPi4uLkC/Py8ubOncv+v37wwQdSqVR+blFR\nERHNmTPnpftFjgU9F2q5Hl3LsYb5hoYGvoTdCzlhwgS+RPmqDP2xoAdgzd0vnBSJRIaGhnyzs5aW\nFhGxfzxENH/+fCK6f/9+R/eoo6PT6WjbV1xcbGho2NZckUgUExNja2vr4+Pz6NEjvvzKlStENHDg\nQL5kypQpRHT16lVq+4TcvHnTzs5O4Ts/b948QQ5k+/btM2bMWLZsGV9y/fr1iRMn+vj4nD592sXF\nZdeuXR9++KH8KoMHDyaikpISQQIA6E1Qy2lILWdra0tET58+5UvYGDRDhw7lS7pSlSHHgt6DfSvY\n1XQNoa2tLZVK21lg4MCBiYmJDQ0NK1as4AtZrZqXl8eXmJqaEpGBgUE7myovL8/NzWUN3bz2966k\n5OTkAQMGKKRQoaGh5eXlU6dO7du378mTJ4no8OHD8guwWpJ7Pp4WAHQdajlhazl7e3sikk/+Hj9+\nTESurq58SVeqMuRY0HuUl5cT0cyZM+n5t+LZs2dEJJPJ2Hgt/JekpaVFfkWFSQGZm5vL/0Oi59WB\nfKVgZ2cXExPDbtBj2P+5lJQUvqSgoICeH1pbbG1tWW9QviQjI0MikXTxEM6fP19UVBQSEsKXsD+a\n7G8lu0XI0tLSxMRE4a85+ztobm7exQAAgIdaTthabuXKlQYGBhcvXuRL0tLSdHV1ly9fzpd0pSpD\njgUap6amhoiqq6vZZGNjI8lVHGxuc3Mzvzxfd1y4cGHChAnr1q2j5y3AO3bsuH///r59+1g1dO7c\nOalUam1t/fjx44cPH7K1vvvuu8GDB3///ffdcSxvvfVWTU0Ni5lhYx8oNDt7enoGBATwk5s3b7a3\nt4+Ojmb/qIjowIEDLi4uGzZsoLZPiIeHx6hRo8LDw9esWRMbG7t169aAgIDVq1cT0a5du+zs7E6c\nONFOqKwXgsI/wtTU1E8++aSlpUUikUgkkujo6MDAwLNnzxIRq4PY6/z8/NLSUtb1lVdWVkb//XcQ\nABjUchpSyxkZGYWGhh46dIh/Rw4fPrx161b5lsIuVWUv7yGmbujz3nN14r2rra3lm0yioqIiIyPZ\n6/Dw8KdPn7L+nkQUHBxcX1/P+nh+9tlnT548KSkp2blzJz9SXFZWlrOzc//+/d3c3LKyslxdXb29\nvU+cONHY2BgSEmJmZvbNN9+wJc+fP29ubn7hwoWOHh0p0RuU/W87d+4cm0xMTJwzZw4RzZ0796ef\nfpJfsqmp6c033+Qnq6urxWKxm5tbUFCQWCz++OOPGxsbOY7j/7G98ITk5ua6u7sbGhqampr6+vqy\nAUI5jvP39xeJREOHDm0rzrS0NF9fXyLS0dH59NNPb9++zXHclStX9PT0WlcabNRjmUwmkUgmTZoU\nFBS0YMGCbdu2yfcb5Tju888/19LSUmaIZPR5h54LtVyPruU4jpPJZEePHvX29g4LC1u8ePHhw4dl\nMpn8ispXZbivEFSqu987hdtqVEyZ2ofjuDlz5vzv//6vCuJpH6uOVbnHefPmrV27VpklkWNBz4Va\njuvttZzyVRnuKwRQtS+++CIlJaW4uFiNMdTV1e3fv58fuVgF0tPTs7Oz5YeJB4DeqhfXcl2sypBj\nQQ/G7i6pra1VdyDtMTU1TUxMDAwMVLgXRpVycnIiIyPHjh2rmt0VFRVFRESkpqYOGjRINXsE6K1Q\nyympO2q5rldlyLF6qh9++GHOnDns4QnTpk2bNm2ak5PT/Pnzjx49yno+9m61tbVhYWGFhYVEtHHj\nxvT0dHVH1J5x48ZFREQcOHBAXQGMHTtWZelOc3Pz8ePH4+LiNOr2csG99tprqn8ao1p22trPP/8c\nEhLCKp9Vq1adOXOmu/d48eLFJUuWsD2uW7eODa3U66GW6xDBazlhqjLBr1wKTjP7Yz18+FDtG2Hf\nvZEjR7JJqVR65swZKyurV1555Y8//uh6eF2nme+dUEi5ngrwUj2iP5bCt9XLy2vr1q3dsaN2dPdO\nO1QjDR8+nIjq6upUEw9rIBk+fHj37a5zUMsBD/2xhJGbmys/eIa6NjJs2DAi6tu3L5vU0tKaP3/+\n5cuXa2trPTw8GhoauhghADCtv60nT54MDw9XcRjdutOO1kjsntP+/furJh62oxfe6AqgsZBjdVhh\nYeG8efOePHmi9o28kLm5eXh4+IMHD9DdGEAQ3fdt1RyadoyaFg9A5/SeHKupqen//u//fH19w8LC\n1q1b98knn7z66qsvXauqqkosFoeEhAQFBc2aNSsoKIiN6Hro0CF27Z+Iqquro6Ki+Mkvv/wyIyOj\nuLh4/fr1HMelp6dv2rRp5MiRxcXFixYtMjIycnBwSExMVH4jLJKbN2++9tpr77///rZt23R0dNh4\naGlpaRYWFpcuXerQqVi8eLGWltb58+fZJBsVd82aNU5OTjNnzrxz5w7HcWfOnPHz87OwsKisrPTx\n8TE2NnZwcLh161Y7wbTeToeiAtAc2dnZixcvDg4OXrly5eTJk//zn/+w8tra2vDwcG9v740bN771\n1lt79+7lOE7h2yqVShMSEnx8fNhA1dRGNdK5b1lb5HfazpYFrJGUr3xUE0/n3tOvv/66f//+IpGI\nDaVLRLGxsX369Pn/2LvvuCiu9X/gz9Ls0vxSFEVAFEFD9KqJxpIoaIxgTJSiQfkZiiZ2uCBBjCYg\nsSEgmlzUoNGgguFeFYnXBFFiwUSjRr9BgUgRkCK9l2Xn98f5OnfvUlxwYWH5vP/gtXOYPfPM7O7Z\nZ2fOnMOmH27epjU1NV29enXjxo0jR47Mzc2dNWvWiBEj2JcCQEd09eXK9pPmardQKJw5c6aDg0NT\nUxPHcVVVVQMHDnzpsyoqKkxNTZbHbjwAACAASURBVLdt28YWCwoKTE1NjYyMWPtobGwsXoP4Ir2Y\nuFsoFMbGxvbt25eI1q5dm5iYGBkZyTZ9/fp1KSthTE1NNTU12dBn9vb2BQUFHMedPXu2X79+58+f\nb20XqNnk6oyenp6WlhZ77Orq+ujRI/bY2tpaR0enrKwsOzubTTIaEBCQmZl54sQJIuJHFmkxmOb1\nlJeXt32E0VMBpNH1/bFGjRplbGzMcVxDQ4O6urqFhQV7PGvWLCcnJ9aMREREEBH79El80LKysviS\n1pqRkpKSDnzK2sBvVCQStVazDFuklzY+/LhNXRNPiyXiWnxNOY7bsmULEfFdVLOyshYtWsQeN2/T\nCgsL+aF3AwMDf/75ZxcXF37MzxahlQNe8/ansybi7mKHDx/+5Zdf7t+/z09RPnTo0NTU1LaftXPn\nzrS0NDYpARHp6Oj4+fk5OzsHBgbu3r1bVVVVfGWJRUZZWdnGxmb48OFpaWk7d+5krUxhYeGmTZvC\nwsLeeustaSphSktLS0tL9+/fv27duq1bt7JW6f3336+oqOjAfOkqKirs5+Cvv/565MgRiSFDrl27\nZmNjM2zYsNTUVNYAjRgxwtPT8/79+60F02I9v/zyizQTnp85c6a98fcUt27dkpikDzrg1q1bXbzF\nTz75hM0+pqysrK2tnZKSQkRhYWGJiYmPHz9mzcjy5cuplQk0xO8zaq0Z+eqrr3bv3t2uT1nbMfMb\nFQgEBgYGLdYswxZJ+sana+J5qRZfUyLatGlTaGhocHAwa74iIyNdXFyolbbx119/ZQGnpqauWrVK\nS0ur7enzeGjlgIhycnIMDAz+q0he6Z70pPmV8P7779N/3+Eizdi4s2bNIiLx3ygZGRlExAb7l6hB\nfJH+++eUxJpPnjwhookTJ7arkjNnzrCfd3/729+SkpLajpxHLf2wq6+vV1VVnT9/PsdxYWFh5ubm\nLT63jdiaB9NGPW1grx2ANNr77mqR9PcVVlZWHjhw4Msvv2T3jnAcZ2trS0RVVVXNV6bWz6l0uBnp\nwEdePIw2apZJi/RSbVTVSfG8NMLmrynz2Wefqaqq5uTkiESiOXPmNDY2cu1pG9uGVg7EKeZ9hWzm\nS4kZKF+K/VrNzMzkS3R1dYlIXV39VYIZOnQo/fcvXWksWbLk/v37c+fO/f3332fMmHHs2LEOB5CQ\nkNDY2DhnzhwiKi4uzsjIkBgXTmLeX2mC6Vg9jJRNVY9DOIsuI13/LfXbb7+NHz/e2Nh469atLNGh\nFw1IWlpau6rqcDMiw4982zrWInUemcdTWFjY2NjY4mvKeHh4qKmpBQcH//7771OmTGEn516lTWtO\n3p+hzkJo5drDzs5O4o2hIDnWqFGjiOjChQt8CZu1u22sy2pcXBxfkp2dTUTs5DA7O8rG8xSJROXl\n5SQ2DTg/C3pzxcXFHajk888/NzExuXTp0smTJ4VCoZ+f30s31KL6+npfX9/XX399/fr1RGRmZsb6\ndfIrJCcn8zNutqZ5MB2rB6B7WrFiRWNjI5u5ViQSERHHcZaWlkS0Y8cOVkJEmZmZP/74I3vc2iex\n7WakDa195GWuYy1S80W5x9MijuM+/fRTZWXlFl9Tts6QIUM++eST8PDw/fv3f/zxx6wQbRp0BTlm\nfFKS5lrh5cuXiWjw4MGRkZH37t07cOAAu/Df9rOqq6stLCyGDRv27NkzVrJ+/fpp06Y1NDRwHLdo\n0SIi8vPzS01N3bdvn6amJhFdvHhRKBSamJj0798/KyuLPYudWGbnnzmO++677yZOnNjeSvr161dS\nUsJxXENDw+DBg1nH2NjY2AEDBvz444+txU9EhoaGfAn7QTxy5Mg///yTldTW1hoZGRHRxx9//P33\n32/ZssXa2pr1VTc0NCQifoJx9uOShd08mDbqaQN6g4I0ur7POxsP+qeffvr+++//53/+h4hu3bp1\n9epVNgjTO++8c+DAAT8/P3d3d9b/XeLTWlFRQUT6+vrcy5qRdn3K2o5ZfKNt1yyTFqntxofjOHYi\nir+02tnx5ObmEtHQoUPZK8KUlZW5ubl99NFHrb2m/CimeXl5ampqs2bN4p/70rax7a7uPLRywGve\n/vSAd4aU7+Bjx46NGjVKS0vLxsYmOTlZygvqFRUVXl5e1tbWHh4eXl5eX375ZV1dHfsXm8G7f//+\n1tbWKSkp06dPd3JyOnXqVF1dnY+Pj56e3g8//MDWZNvas2fP8+fPCwoKvvrqK/7DKX0lRDRhwoSv\nvvpq2bJlCxYsSE9P5zjup59+0tfXv3z5cvPIr127xv8gmzVr1ty5c21tbT/88MMDBw5INA0ZGRm2\ntraampq6urpubm6FhYUcx/E/1/z9/cvKyoKDg9ni5s2ba2pqWgymxXrahtYHpNH1OdaBAwcGDx48\nefLkpKSkkJAQDQ2NhQsXFhUVPXjwYO7cuRoaGkOHDt2wYUNZWRlbX/zTWlVV5ePjwz4vQUFB5eXl\nrTUjHfiUtUZio4GBgW3ULJMWqY3G55dffvH29mZbXLZs2dmzZ9ve01eP5/LlywsXLmR1jhkz5u23\n33777bdHjx6tpqZGRMeOHWvjNeXDXrBgwfHjx8V3pHmbVlVV9cUXX7ANubm53b1796XvJbRywGve\n/gi4F2dTu63o6GgHB4f2xmlmZpaSktI1e9eV2+pZOvba9RQCgSAqKsre3l7egfR4MnyfsJcjOjr6\n1avqubpbi9Qd4qmurra0tHzw4IHMB6ZHKwe85u2PgvTHaoOgdY8fP5Z3dAAA/weNVec5ePDgunXr\nOm/mH4AWKcj4WBJEIlFJSQm96AHQ2Ztj/aL4gU8BADpAVo1Vd2uR5BjPrVu33N3da2pqmpqakKdC\n11PA81iPHj3atGkTm+jKx8enqqqq87ZVVVXl6+ubk5NDROvXr09KSuq8bQEAtK27tUhyj2fAgAEV\nFRVKSkonT57s06dPF28dQAFzrLFjx4aGhrLuZkFBQZ36y2ngwIGBgYFsWxEREVOnTu28bYEiSU1N\n3bt3b1RUlKWlpUAgsLCwYJ2gmfj4+Hnz5gkEgkmTJsllhMPc3NyIiAh7e3uJtzTHcd9++62dnd2W\nLVtcXV1PnjzJyoVCobe3N/sqBTnqbi2S3OMZP358ZmZmamqq3A9Fb4BmrQWd2MNeRhT7rg3F1tmv\nHX9jtlwqoY7ecXPlypWlS5fW19dzHMcGBCIiNzc38XXYWOGPHz/uWGyvTnxKPt4XX3xhaGjIRhwo\nKSkxNDRksyZzHFdUVPTBBx88efKkA9vq+vsKAWQFrRyDZo1rqf1RwPNY0EtkZGQsW7asO1TSLsnJ\nyStWrAgLC2O3nbNBfWbMmHH48GHx33ZsMhA2fo9cjBgxQqIkKyvL399/1apVbBAjTU1NNzc3X19f\nNqSktrb2tm3bFi5c2KlX5wF6lZ7SyqFZaw1yLOiRcnJybGxsWK87+VbSLiKRyMnJaeXKldra2uLl\nUVFRenp67u7u6enprIRNjssarG4iMjJSKBSyOZqY2bNn19TU8LPqWlpampiYeHl5ySlAAIXSU1o5\nNGttQI4F8ldeXu7l5eXj4+Ph4TF37lwPD4/S0lIiCg8PZzeuE1FFRUVQUBC/eOzYseTk5Pz8/NWr\nV3Mcl5SU5OnpOXLkyPz8/MWLF2tpaY0bNy4mJkb6SlgkCQkJBgYGiYmJnbSn58+fv3fv3rvvvitR\nrq+vHx0dXV1d7ejo2NDQIOUh4jju3Llz7u7uBgYGpaWlzs7O2tra48aNu3PnDnsWmyrExcVl0qRJ\nVlZWDx8+fJXgr1+/TkTis8qzkb7/+OMPvmTevHmHDx9mk/4CAE+BWzk0a2151cubnQ/9sXouaV67\niooKU1PTbdu2scWCggJTU1MjIyP2YTM2NhavQXyRXlxWFwqFsbGxffv2JaK1a9cmJiZGRkayex2u\nX78uZSXM2bNn+/Xrd/78eWn2jtrfH8vR0ZFeTDAiXg97sG/fPiLy9PSUKG/tEJWUlGRnZ7NpowIC\nAjIzM0+cOEFE/Kwsrq6ujx49Yo+tra11dHReOv2RRGDiB4dN51dTU8OXsHvy33zzTb7k7t27RMR3\nc5YS+mNBz4VWDs0aT5Hn0oFuSJrXztfXl4j4ud44jvvuu++IyMvLi3sxTxH/L/FFiY+Kqakpic2e\nxmbzYOMvS18Jx3H8rGov1YEcy9DQUF1dvXk97IFIJFqyZAkRxcXFiZe3fYhGjx4tXoOOjo6amhrH\ncbdu3Wr+myo2Nlb6aCUOzowZM4iotraWL2E3DU2cOJEvYZPKzZ8/X/qtcMixoCdDK4dmjYc+79Dt\n3Lhxg4gGDRrEl8ycOZOIbt682a56lJSUiIj9+iEiNrtZWlpae+NRUenEgXnz8/NZz8oWCQSCiIgI\nMzMzZ2fnZ8+e8eVtHyJ2RYCvQVNTk52Wv337trm5uUQTYGNj0+HgzczMiKisrIwvYRc72Oy/jIaG\nBhEVFBR0eCsAikexWzk0a21AjgVyxlqNzMxMvkRXV5eI1NXVX6Va9glhV9a7D2Vl5aampjZWGDRo\nUExMTG1t7UcffcQXduwQFRcXZ2RksPPevLa33jYLCwsiEm8l8/LyiGj69Ol8CWsZOQWduw2gYxS7\nlUOz1gbkWCBn7LdLXFwcX5KdnU1EVlZW9OLNXV9fT0QikYgNu8K/14VCYWvVsjtvO1BJG3W+On19\nffEfTPSidRBvI8zNzSMiIq5evcqXtH2IWmNmZsY6h/IlycnJBw4c6HDwy5cvV1dXv3LlCl+SkJCg\nqqoqfls4+wmor6/f4a0AKB7FbuXQrLVFmuuX8oX+WD2XNK9ddXW1hYXFsGHD+Avz69evnzZtGutB\nuWjRIiLy8/NLTU3dt28fOyN98eJFoVBoYmLSv3//rKws9izW/4DvZ/Ddd99NnDixvZXExsYOGDDg\nxx9/lGbvqP39sT7++GMiqqio4EvY76fc3FyJNTdu3MgfurYPkaGhIRGJRCL2L/bTtqGhoba2lo1D\n8/HHH3///fdbtmyxtrZmnUP37NkzduzYkydPthEq+6U4atQo8cKdO3eampqy+MvLy0eNGvXFF1+I\nr8BuxkGfd+g90MqhWeOhzzt0KSlfu4qKCi8vL2traw8PDy8vry+//LKuro79KyUlZcqUKf3797e2\ntk5JSZk+fbqTk9OpU6fq6up8fHz09PR++OEHtiZrffbs2fP8+fOCgoKvvvqqsrKyvZX89NNP+vr6\nly9flmbvOpBjsZ9xly5dYosxMTHz588nogULFvzyyy/iazY0NLz11lsvPUT8Dzh/f/+ysjLWB5aI\nNm/eXFNTk5GRYWtrq6mpqaur6+bmVlhYyGr75JNPBALB0KFDW4szISHBzc2NiFRUVHbt2nXv3j1W\nLhKJjhw54uTk5Ovru2TJkkOHDvGNIPP1118rKSm1d7R35FjQc6GVQ7PGQ44FXaorXzuJ22q6QAdy\nLI7j5s+fv2HDhs6Ip11Yiyzzam1sbFxdXdv7LORY0HOhlePQrL2A+woB5Ozo0aNxcXH5+flyjKG6\nunr//v38QMaykpSUlJqaGhQUJNtqAaCbQ7PWGuRYoCDYhfbuP1merq5uTEzMpk2bJG6N6Urp6emB\ngYHjx4+XYZ25ubk7duyIj49nU5UBgMx121YOzVprkGNBj1dVVeXr65uTk0NE69evT0pKkndEL/Ha\na6/t2LHj4MGD8gpg/Pjxss2EGhsbT5w4cfLkSbnfRg6gkLp/K4dmrUWdONwiQNcYOHBgYGBgYGCg\nvANpB2NjY29vb3lHITOqqqo+Pj7yjgJAYfWIVg7NWnM4jwUAAAAge8ixAAAAAGQPORYAAACA7CHH\nAgAAAJC9HtPn3d7eXt4hQLux+acU7LWrrq5WU1NTVVUlouDg4B9++EHeEfV47H0iK0lJSQr2llMk\nDQ0NQqGwf//+8g5EZhSylROHVk56SUlJU6dOFS8RcB2aSrorZWZmfvbZZ68ysTaADN28ebOoqMjC\nwsLY2JjNwwqvzsDAYN++fa9ez5kzZ86cOfPq9YDMiUSiv/7669GjR3p6em+88Ya8wwHoFGyod36x\nB+RYAN1KWVnZzp07Q0JCRo4c6e/vL/5xAoAWxcfHb9iw4a+//lq9evWXX36prq4u74gAugL6YwG0\nj4aGxs6dO//3f//3tddes7e3t7a2fvDggbyDAuim7t69+/bbb1tbWxsbGz969Cg0NBQJFvQeyLEA\nOmLUqFHR0dFXrlwpLi6eMGHCihUrCgoK5B0UQDfy7NmzVatWTZkypaam5tq1a7GxscbGxvIOCqBL\nIccC6Li33377zp07R48ejY+PHzVq1Pbt2+vq6uQdFICc1dTU7Nq1y8zM7OLFixEREb/++uv06dPl\nHRSAHKA/FoAMVFdXHzhwICAgQEtLy9/ff/ny5egOD70Qx3E//PCDl5dXcXGxp6enj49P37595R0U\ngNzgPBaADAwYMGDz5s2PHj169913V65cOW3atFu3bsk7KIAudeXKlb/97W+Ojo4zZ87866+/tm/f\njgQLejnkWAAyY2BgEB4efuvWLRUVlWnTptnb2z99+lTeQQF0urS0NHt7+9mzZ2tra9+7d+/48eO6\nurryDgpA/pBjAcjY5MmTr127du7cuTt37owdO9bHx6eqqkreQQF0itLSUh8fn/Hjxz98+DA2Nvbn\nn39+7bXX5B0UQHeB/lgAnaWhoeGbb775/PPPBw0a9Pnnn7u6uiop4VcNKIjGxsajR4/6+fmJRKKt\nW7euWbNGRaXHTBwC0DXQ4gN0FjU1tQ0bNjx58mTx4sWffvrpG2+8ce3aNXkHBSAD8fHxr7/++rp1\n65YuXfrkyZMNGzYgwQJoDjkWQOcaMmRIaGjogwcPhgwZMnPmTFtb24yMDHkHBdBBGFMUQHrIsQC6\ngrm5+cWLF3/++ef09HQzM7MNGzZUVFTIOyiAdsCYogDthRwLoOtYWVndv38/LCzs5MmTJiYmoaGh\nmOwcuj+MKQrQMejzDiAHJSUlu3fvDg4ONjExCQoKmj9/vrwjAmgBxhQFeBU4jwUgB1paWjt37nzw\n4MG4cePee+89a2vrP//8U95BAfwXjCkK8IqQYwHIzZgxY6Kjo+Pj4wsLCydMmLBq1arnz5/LOygA\njCkKIBvIsQDkbM6cOffu3Tty5Mi5c+fMzMx27drV0NAg76Cgl8KYogAyhP5YAN1FVVXV3r17d+3a\nNWLEiICAADs7O3lHBL0IxhQFkDmcxwLoLgYOHLh9+/aUlJQ33njDwcHBysrqwYMH8g4KegWMKQrQ\nGZBjAXQvI0aMOH78+M2bN2tqaiZMmLBixYqCggJ5BwUKC2OKAnQe5FgA3dGbb75548aN06dPJyYm\njho1avv27XV1dfIOChQKxhQF6GzIsQC6KYFAYGdn9+jRIz8/v6CgoNGjRx8/fhwdKOHVYUxRgK6B\nPu8APUBubu6XX3555MiRyZMnBwcHT506Vd4RQY8kEoliYmIwpihA18B5LIAeYNiwYeHh4b/++qua\nmtpbb71lb2+flZUl76Cgh7ly5cqkSZMwpihAl0GOBdBjTJo06Zdffjl37tzvv/9ubm7u4+NTWVkp\n76CgB8CYogBygRwLoIextbV99OhRYGDgN998M3bs2EOHDolEInkHBd0UxhQFkCP0xwLoqYqKivz9\n/Q8ePPj6668HBwfPmDFD3hFBN4IxRQHkDuexAHqqIUOGhIaGPnz4UEdHZ+bMmba2tunp6fIOCroF\njCkK0B0gxwLo2caOHfvjjz/+/PPPGRkZY8eO3bBhQ3l5ubyDArnBmKIA3QdyLABFYGVlde/evbCw\nsFOnTpmYmISGhjY1Nck7KOhSGFMUoLtBjgWgIFRVVd3d3VNSUlxdXb29vcePH//jjz/KOyjoChhT\nFKB7Qo4FoFA0NTV37tz58OHDcePGLViwwNra+s8//5R3UNBZRCLRmTNnxo4dGxAQ4OHhkZqaumLF\nCoFAIO+4AIAIORaAQho9enR0dHR8fPzz588nTJiwatWq58+fyzsokDF+TNFZs2ZhTFGAbgg5FoDC\nmjNnzt27d48cOXLu3LkxY8bs2rWrvr5e3kGBDGBMUYAeATkWgCJTUlJasWLFkydP1q9fv3379tde\ne+3MmTPyDgo6DmOKAvQgyLEAFN+AAQO2b9+empr6xhtvODg4zJkz548//pB3UNA+jY2Nhw4dGjNm\nzJEjR3bt2vXw4UMbGxt5BwUAbUGOBdBbDB8+/Pjx40lJSXV1dRMnTlyxYkV+fn5rK1dXV3dlbNDQ\n0NDGfzGmKEBPhBwLoHd54403rl+/fvr06V9++WXUqFHbt2+vq6uTWCc7O1tXV9ff318uEfZCKSkp\nenp6ISEhzf+FMUUBei7kWAC9jkAgsLOzS05O3rp16759+0aPHn38+HHxqUt9fHxqa2u3bdt2/Phx\nOcbZSxQUFFhbW5eVlW3durWoqIgvx5iiAD0dciyAXqp///6bN29+/Pjx/PnzV65c+eabb968eZOI\nfvvtt1OnTolEIo7jPv74459//lnekSqympoaGxub/Px8juPq6+u3b99OGFMUQFEIxH+8AkDv9Pvv\nv3t4eFy7dm3JkiV//fXXw4cPhUIhESkpKfXt2/fmzZuWlpbyjlEBNTU1LVq06N///jc72kSkpKS0\nZ8+e0NDQkpIST09PHx8fDHkF0HMhxwKA/xMbG+vq6vr8+XPxZkFFRUVLS+vOnTvDhw+XY2wKac2a\nNeHh4eIzS6qqqqqqqi5evHjPnj0Y8gqgp8O1QgD4P9bW1ioqKhIzsQiFwtLSUmtr6/LycnkFppB2\n7NjxzTffSEzd3djYWFNTs3z5ciRYAAoAORYA/J/g4OCCggKRSCRR3tjYmJ6ebmtr2/b4AiC906dP\nb926tcXLCMrKyhs3bpTIvQCgJ8K1QgAgIiosLDQyMqqpqWltBWVl5eXLlx89erQro1JIV69enTt3\nbmNjY2srKCkpHTp0yMXFpSujAgCZw3ksACAiCgsLq6mpaWNky6ampuPHj2PQrFfExmd/6WmqL774\nomviAYDOo8xuFQaAXs7U1NTQ0FBPT6+pqam0tLSpqUkgEPTp00f80iHHcVevXjUyMsJthh2Tk5Mz\nY8aMyspK8RxLRUVFSUmJHechQ4ZMnTp18eLFa9asGT16tPwiBQAZwLVCkFZmZubt27flHQV0BZFI\nlJeX9/Tp08zMzKysrIyMjNLSUiJSVlZuampSVlbeunWrubm5vMPsYerr6318fHJzc9lhJCJVVdWh\nQ4eamJgYGhqOGDFixIgRgwYNkneY0BUmT548cuRIeUcBnQ45Fkhr6dKlp0+flncUAAA9nqOj46lT\np+QdBXQ6zCoK0mpqarKzs4uOjpZ3INAt2NvbE5FCvh+io6MdHBzw+xM6ib29Pe4b7SXQ5x0AAABA\n9pBjAQAAAMgeciwAAAAA2UOOBQAAACB7yLEAAAAAZA85FgAAAIDsIccCAAAAkD3kWADQdd544w0v\nLy95RyEzAoFASUnJ29t7586dqampfHlqaurevXujoqIsLS0FAoGFhYX4ZNvx8fHz5s0TCASTJk2K\niorq+rBzc3MjIiLs7e2nTp0qXs5x3LfffmtnZ7dlyxZXV9eTJ0+ycqFQ6O3tnZOT04FtKfyhSE1N\n3blz57p16wQCgUAg6NLoofvjAKRjZ2dnZ2cn7yigu+jY+8HBwcHPz68z4mGePn366pWwL3tp1iQi\nExMTicIrV64sXbq0vr6e47jy8nLW0rq5uYmvk5GRQUSPHz9+9Wg7Jisri4jGjBkjXvjFF18YGhqW\nlJRwHFdSUmJoaBgSEsL+VVRU9MEHHzx58qRdW+lVh8LQ0FDKtw3a0t4DORZIC+0CiOuG74f09PTp\n06e/ej3tyrEkvpv//PPP4cOHFxUVia8zY8YMIjp9+jRf2NDQQEQs+ZAXieAzMzNVVFQCAwP5koCA\ngP79+/P7cv/+fQsLi8rKSinr722HYsyYMcixQAKuFQKAIsjJybGxsXn+/LkcYxCJRE5OTitXrtTW\n1hYvj4qK0tPTc3d3T09PZyWqqqpEpKamJocoWxEZGSkUCufMmcOXzJ49u6am5siRI2zR0tLSxMRE\nyku9OBQAhP5YANA1mpqaoqOjnZ2dZ86cyXHcuXPn3N3dDQwMSktLnZ2dtbW1x40bd+fOHY7jkpKS\nPD09R44cmZ+fv3jxYi0trXHjxsXExBBReHg43+uloqIiKCiIXzx27FhycnJ+fv7q1avZFhMSEgwM\nDBITE7tsH8+fP3/v3r13331XolxfXz86Orq6utrR0ZGdtpFQXl7u5eXl4+Pj4eExd+5cDw+P0tLS\nNo4Se1Ztbe2uXbtcXFwmTZpkZWX18OHDVwn++vXrRGRgYMCXDB8+nIj++OMPvmTevHmHDx9+8uTJ\nS2vDoQAgQn8skBrOb4O4Drwf+I4vIpEoOzt7wIABRBQQEJCZmXnixAkimjJlilAojI2N7du3LxGt\nXbs2MTExMjJy4MCBRHT9+nWO44yNjcUbLvFF+u8rPmfPnu3Xr9/58+fbu2sdvlbo6OhIRA0NDRLr\nsAf79u0jIk9PT4nyiooKU1PTbdu2scWCggJTU1MjI6OSkpLWjhJb09XV9dGjR+yxtbW1jo5OeXm5\n9LspEbylpSUR1dTU8CXV1dVE9Oabb/Ild+/eJSLxi2it6YWHAtcKoTnkWCAttAsgrgPvB5FIJP5l\nNnr0aP47SSQS6ejoqKmpsUVTU1MiqqqqYovBwcFE5ODgwDX7JhNfpGa9oxobG9u9Y6+QYxkaGqqr\nqzdfhz0QiURLliwhori4OPFyX19fInr27Bn/lO+++46IvLy8uNaP0q1bt5r/Zo6NjZV+NyWCZz2l\namtr+RJ2A+DEiRP5ktzcXCKaP3/+SyvvhYcCORY0h2uFANBFJO5sF18UCASampr8xSMlJSUiYuct\niGjhwoVElJaW1t4tqqiodIN3ggAAIABJREFUdDjaDsjPz9fU1GztvwKBICIiwszMzNnZ+dmzZ3z5\njRs3iGjQoEF8ycyZM4no5s2b1PpRun37trm5uUSDbmNj0+HgzczMiKisrIwvKS0tJaKhQ4fyJRoa\nGkRUUFDw0tpwKAAI/bEAoPtj322sT0x3pqys3NTU1MYKgwYNiomJqa2t/eijj/hCllBmZmbyJbq6\nukSkrq7eRlXFxcUZGRnsGhav7a23zcLCgojEM568vDwimj59Ol/CshyO415aGw4FACHHAoDur7i4\nmIisrKzoxXdbfX09EYlEIjbqEv9VJxQKxZ8osdjZ9PX1xU9+0ItvevHve3Nz84iIiKtXr/Il7FRN\nXFwcX5KdnU0v9rc1ZmZmrKM3X5KcnHzgwIEOB798+XJ1dfUrV67wJQkJCaqqqsuWLeNL2OkcfX39\nl9aGQwFAyLEAoMtUVlYSUUVFBVusq6sjsfSI/bexsZFfn8+QLl++PHHixFWrVtGL6zgBAQFpaWmh\noaEs2bp06VJTU5OJiUleXt7Tp0/Zsy5cuKChoXHx4sUu2TkiolmzZlVWVrIdYQoLC6nZFSV7e/uN\nGzfyi97e3hYWFmFhYexkCREdPHhw2rRpa9eupdaP0vvvv29kZOTv7+/i4hIZGenn57dx48aVK1cS\n0d69e83NzU+dOtVGqKyDkXjGo6Wl9dlnn4WHh/Mv06FDh/z8/MRPHxYVFdGL0zltb6VXHQqAVnV+\nly9QEOinCeLa+36oqqry8fFhzU5QUFBgYCB77O/vX1ZWxnq1E9HmzZtrampY9+E9e/Y8f/68oKDg\nq6++4sd7TElJmTJlSv/+/a2trVNSUqZPn+7k5HTq1Km6ujofHx89Pb0ffviBrfnTTz/p6+tfvny5\nvbvW4T7v7JTMpUuX2GJMTMz8+fOJaMGCBb/88ov4ExsaGt566y1+saKiwsvLy9ra2sPDw8vL68sv\nv6yrq+M4jj8Z0+JRysjIsLW11dTU1NXVdXNzKywsZLV98sknAoFg6NChrYWdkJDg5uZGRCoqKrt2\n7bp37x4rF4lER44ccXJy8vX1XbJkyaFDh0QikfgTv/76ayUlJTbEedtb6VWHgkGfd2hOwOFyMkjH\n3t6eiKKjo+UdCHQLnfp+MDMzS0lJkVfrFB0dze5hfOmaAoFgzJgxjx8/5kvee++90aNHh4SEdGaA\nL5eamrp8+fJff/1VttXa2trq6ekdPnxYmq30qkNB7XnToi3tPXCtEACg49jFSt7Ro0fj4uLy8/Pl\nFQ8RVVdX79+/nx+UXFaSkpJSU1ODgoKk3ErvORRMF3f+gx4BORZ0UwUFBdHR0Tt27Ogm9XQHveeY\nsHvEqqqq5B3Iy2VmZq5fv37nzp2pqalEpKurGxMTs2nTJonb3LpSenp6YGDg+PHjZVhnbm7ujh07\n4uPjBw8eLOVWesmhSE1N3blzp7e3N8Z8hxbI91Il9CBd2YcgOTn5008/pWZDSoqbMmXK3//+91ev\nRxoikejw4cOWlpYDBgx47bXXvv32W4nOGS2KiYlhAy0SUUJCQvMV2JQdRPThhx+2uIK47nZMOun9\nUFlZ+dlnn7HDsnLlyps3b8p8Ey8lfX+s1jx58mTXrl2yikfuGhoavvrqq3aNnM7DoWgO/bF6D/TH\nAml1cR+Curq6fv36iXd2yc7OFr+vx9HR0dTU1N/fv731dICPj09OTs7UqVNTU1MPHTpUV1e3f//+\ndevWvfSJNTU1bCBNW1vb8+fPS/x36dKl586dq62tzcvL09PTe2lt3eqYKHCfEun7YwF0gAJ/dkBC\nlw6CDCA9NmMdLyMjY8WKFdeuXeNLTp8+3YF6OiA7Ozs7OzsyMpItvvfee++++25oaKg0OVb//v2J\naNq0aRcuXEhLS2NTxDB5eXklJSUjRoxISUmRJsGi7nRMAADgpdAfC3qAnJwcGxub58+fy2XrWVlZ\n4p1b586dO2TIEDbYj5Q2btzIcVxoaKh44aFDhz755JMORyXfYwIAAC+FHAtkqbq6+vvvv1+6dOm0\nadOSkpImTJhgaGh4/fr1lJSURYsWDRkyxMzM7M6dO2zl8PBwgUDAhu2uqKgICgriFyUcO3YsOTk5\nPz9/9erVRNTU1BQdHe3s7Dxz5kyO45KSkjw9PUeOHJmfn7948WItLa1x48bFxMQ0r+f777/v37+/\nQCDYuXMnuwkoMjJSTU2NzTvbmunTp0ucZ2poaGCzxhJRQkKCgYFBYmJiGzV88MEHI0aMOHr0KBsb\nmtVw6dIlW1tbiTV7yjEBAICXk2tvMOhJpOmn2dTUxCbuHTx48IULF/78808iMjQ03L17d1lZ2d27\nd4lo1qxZ/PrGxsbib0KJRRLrl03/3Uc7KyuLlQiFwtjYWHbxa+3atYmJiZGRkQMHDiSi69evN3/u\nli1biOh///d/+XoWLVrUruNw/fr1vn37/v7772zx7Nmz/fr1O3/+fGvrsz3as2cPEfGdf0+dOrVn\nzx6upXELe8oxUeB+u6/e5x2gDQr82QEJ6I8FsqSkpGRiYkJE+vr6CxYsIKKhQ4dmZWV5eXkR0euv\nvz5kyJD79+/z66uqqoo/XWKxDXxHb2VlZRsbm+HDh6elpe3cuZN1MC8sLNy0aVNYWNhbb70l8cRN\nmzaFhoYGBwezIXMiIyNdXFyk30GhUOjr6xsRETFx4kRW8v7771dUVKiovOSj5Orqun379rCwsE2b\nNqmqqkZERLAv8uZ60DFJSkpivXcVDJsjTyF3DbqDpKSkqVOnyjsK6Aq4VggyJnFha9CgQeL/0tbW\nZpP4ynYrSkpKRMSSCSJauHAhEbEzahK0tbXXrVt3/Pjx3NxcjuMuX7787rvvSr/dL774Ys6cOUuX\nLhUvfGmCRUQaGhorV67MycmJiYm5d++esbGxpqam9NuVhryOCQAAtAjnsUABDR06lMTO60jw8PDY\nv39/cHCwo6PjlClTpMmQmNjY2AEDBvCT7rXX+vXrDx48GBwcPH78eA8Pj45V0mGdcUymTp2qkPef\ns7EbFHLXoDvAKdLeAzkWyBM79VJfX9+nTx+RSMROcXEc12Ivb+mnqiguLiYiKyurFv87ZMiQTz75\n5B//+EdhYeHnn38uZZ0//fRTbm6ueIJ18+bNadOmscBaS0qamprYX2VlZVNTUxsbm9jYWC0tLXNz\nc7YC12wQph50TAAAoA24VggyVltbS2KpQ2NjIxFVVlayxbq6OnqReRCRmZkZEQUEBKSlpYWGhrKp\n3y5dutTU1FRTU8OvT0QmJiZ5eXlPnz5li6zCiooK8U3zCcfly5cnTpy4atUqIpKoh/H09GxoaHj6\n9OmoUaOk2an4+Hh2292BAwcOHDjAulX9+OOPRHThwgUNDY2LFy+2+EQ2xENBQQFb3LRpExGxYdYZ\nNs0IO2g965gAAEDbkGOBLBUUFLCb1DIzM+Pj4y9dusRudtuyZUtxcXFYWBhbDAoKKioqIqJdu3ZN\nmTJl3759a9asWbBggYWFhZOTU1lZWWpqKjtjlJWVFRISUlpaamdnN3jw4Nu3bxNRdXV1YGAgEeXl\n5e3bt4/PKkJCQoqKigoLC589e5aYmKiqqpqeni5RD1tTT0/P2tpayp7dN2/eXLhw4ZUrV9a9sH79\n+pCQkI8//piI+vTpM3jw4D59+jR/4rlz59zd3YnI3d398uXLRPT2229/8MEH7733HhElJydv2bIl\nNzeXiJydna9cucKe1SOOCQAAvBTm0gFpdef5H8zMzFJSUqR/M1dXV1taWj548ICNw66QOvuYdOf3\nwyvCXDrQqRT4swMScB4LeqODBw+uW7dOPJkQtO5VJvXrQZofEwAAeBXo8w6KgPVqqqqqYiNttubW\nrVvu7u41NTVNTU0SmZPinbR49WMCAACvAuexoGerqqry9fXNyckhovXr1yclJbWx8oABAyoqKpSU\nlE6ePNliDyrFgGMCTGpq6t69e6OioiwtLQUCgYWFBbvdgYmPj583b55AIJg0aVJrI+J2qtzc3IiI\nCHt7e4kBOTmO+/bbb+3s7LZs2eLq6nry5ElWLhQKvb292RsboGeQz/Dy0ANh/gcQ19nvh6dPn8qr\nEsWYS+fKlStLly6tr6/nOI4f+NfNzU18nYyMDCJ6/PixnGL8zwRQ4oVffPGFoaFhSUkJx3ElJSWG\nhoYhISHsX0VFRR988MGTJ0/kEKvsoC3tPXAeCwC6nYyMjGXLlnWHSnqo5OTkFStWhIWFqampEdHg\nwYOJaMaMGYcPHxY/ZTVs2DAiMjIyklecI0aMkCjJysry9/dftWoVmwhBU1PTzc3N19eXDfCmra29\nbdu2hQsXVlVVySFcgHZCjgUA3UtOTo6Njc3z58/lXkkPJRKJnJycVq5cqa2tLV4eFRWlp6fn7u6e\nnp7OSthsmCwP6yYiIyOFQuGcOXP4ktmzZ9fU1LDJNInI0tLSxMSETYEK0M0hxwKATlReXu7l5eXj\n4+Ph4TF37lwPDw82Ild4eDi7bZOIKioqgoKC+MVjx44lJyfn5+evXr2a47ikpCRPT8+RI0fm5+cv\nXrxYS0tr3LhxMTEx0lfCIklISDAwMEhMTJTXoegy58+fv3fvXvNJJ/X19aOjo6urqx0dHRsaGpo/\nscUXi+M4NtKbgYFBaWmps7Oztrb2uHHj7ty5w55VW1u7a9cuFxeXSZMmWVlZPXz48FWCv379OhEZ\nGBjwJWwCqD/++IMvmTdv3uHDh588efIqGwLoCvK+WAk9BvoQgDhp3g8VFRWmpqbbtm1jiwUFBaam\npkZGRuyb29jYWLwJEl+kF310hEJhbGxs3759iWjt2rWJiYmRkZHsTsnr169LWQlz9uzZfv36nT9/\n/qW71tP7Yzk6OhJRQ0ODeCG/R/v27SMiT09PifLWXqySkpLs7Gw2uXhAQEBmZuaJEyeIaMqUKWxN\nV1fXR48escfW1tY6Ojrl5eXSRyvxMllaWhJRTU0NX8LukH3zzTf5krt37xJRYGCg9FvpVtCW9h49\nuB2BLoZ2AcRJ837w9fUlomfPnvEl3333HRF5eXlxHDdmzBjxVEZ8UeJ719TUlIiqqqrYYnBwMBGx\nYUKlr4TjuMbGRml2rafnWIaGhurq6hKF/B6JRKIlS5YQUVxcnHh52y/W6NGjxWvQ0dFRU1PjOO7W\nrVvNf7rHxsZKH63EyzRjxgwiqq2t5UvYvZATJ07kS9jsCPPnz5d+K90K2tLeA9cKAaCz3Lhxg4gG\nDRrEl8ycOZOIbt682a56lJSUiIidSiGihQsXElFaWlp742lt6m4Fk5+fzzqMt0ggEERERJiZmTk7\nOz979owvb/vFEp+SXCAQaGpqsquNt2/fNjc3l/hesbGx6XDwbL7OsrIyvoRdXB46dChfoqGhQWLT\ngAJ0W8ixAKCzsNwoMzOTL9HV1SUidXX1V6mWfd2ybjrQnLKyMj/teosGDRoUExNTW1v70Ucf8YUd\ne7GKi4szMjLY5Txe21tvm4WFBRGJJ395eXlENH36dL6EJXycwo0bDIoHORYAdBZ2IiQuLo4vyc7O\nJiIrKyt68U1ZX19PRCKRiI3hxH9xCoXC1qplt/F3oJI26lQk+vr64ueB6EXSI576mJubR0REXL16\nlS9p+8VqjZmZGevzzpckJycfOHCgw8EvX75cXV2dnyKdiBISElRVVcWH4WBntvT19Tu8FYCugRwL\nADqLt7e3hYVFWFgYOxVBRAcPHpw2bdratWvpxVWhgICAtLS00NBQliddunSpqanJxMQkLy/v6dOn\n4rXxGdLly5cnTpy4atWqdlVy4cIFDQ2Nixcvds2+y9GsWbMqKysrKyv5ksLCQmp2cc3e3n7jxo38\nYtsvVl1dHYklr6zyxsbG999/38jIyN/f38XFJTIy0s/Pb+PGjStXriSivXv3mpubnzp1qo1QWV8r\n8eRPS0vrs88+Cw8PZ5uoqKg4dOiQn5+f+GnLoqIi+u8zWwDdE3IsAOgs/fv3T0pKWrZsmbOzs6en\np7e395AhQ9hpCSLatWvXlClT9u3bt2bNmgULFlhYWDg5OZWVlQmFQjs7u8GDB9++fVu8tpCQkKKi\nosLCwmfPniUmJra3kj59+gwePLg3zBe0YsUKIuLnUPrnP//p4uJCRO7u7teuXRNfc/fu3W+99RZ7\n3MaLdfDgQTYg+44dO8rLy0NCQti1vK1bt3Icl5CQYGtr+69//cvT07OwsDAyMpINeZqenv748eO/\n//3vrcV55coVluRlZmbu3r37/v37rNzb23vz5s2ffvrpli1bXFxcvL29t27dKv7EGzduKCkpOTg4\nyOZ4AXQaAS5pg5Ts7e2JKDo6Wt6BQLfQle8HMzOzlJSULmusoqOj2U2LXbO5zvDee++NHj06JCRE\nvmGkpqYuX778119/lW21tra2enp6hw8flm21XQZtae+B81gAAIrm6NGjcXFx+fn5coyhurp6//79\n/PjsspKUlJSamhoUFCTbagE6A3IsAOju2G1rmKJOerq6ujExMZs2bZK4468rpaenBwYGjh8/XoZ1\n5ubm7tixIz4+nl2OBOjmkGMBQPdVVVXl6+ubk5NDROvXr+f7GMFLvfbaazt27Dh48KC8Ahg/frxs\nM6HGxsYTJ06cPHkSw3ZAT9ErRuQDgB5q4MCBgYGBgYGB8g6kRzI2Nvb29pZ3FDKjqqrq4+Mj7ygA\n2gHnsQAAAABkDzkWAAAAgOwhxwIAAACQPeRYAAAAALKHHAsAAABA9nBfIUhLWVn59OnTbApeAEaB\n3w8KvGsgd46OjvIOAboC5tIBaWVmZkrMHwfQ3QQHBxPRpk2b5B0IQFsmT548cuRIeUcBnQ45FgAo\nDswEBwDdB/pjAQAAAMgeciwAAAAA2UOOBQAAACB7yLEAAAAAZA85FgAAAIDsIccCAAAAkD3kWAAA\nAACyhxwLAAAAQPaQYwEAAADIHnIsAAAAANlDjgUAAAAge8ixAAAAAGQPORYAAACA7CHHAgAAAJA9\n5FgAAAAAsoccCwAAAED2kGMBAAAAyB5yLAAAAADZQ44FAAAAIHvIsQAAAABkDzkWAAAAgOwhxwIA\nAACQPeRYAAAAALKHHAsAAABA9pBjAQAAAMgeciwAAAAA2UOOBQAAACB7yLEAAAAAZA85FgAAAIDs\nIccCAAAAkD3kWAAAAACyhxwLAAAAQPZU5B0AAMArycrKampqYo+rq6uJKD09nS0qKysbGhrKLTIA\n6N0EHMfJOwYAgA5KSEiYM2dOGytcvnx59uzZXRYPAAAPORYA9GBlZWU6OjqNjY0t/ldVVbWwsFBD\nQ6OLowIAIPTHAoAeTUNDY/78+SoqLXR7UFFRee+995BgAYC8IMcCgJ7NycmJ748lTiQSOTk5dX08\nAAAMrhUCQM9WV1c3ZMgQ1ttdXP/+/YuKivr16yeXqAAAcB4LAHq2vn37fvjhh6qqquKFqqqqS5Ys\nQYIFAHKEHAsAerxly5ZJdHtvbGxctmyZvOIBACBcKwQABSAUCnV1dUtKSvgSDQ2N58+ft9gXHgCg\na+A8FgD0eCoqKkuXLuUvF6qqqjo5OSHBAgD5Qo4FAIpg6dKl/OXCxsbGpUuXyjceAABcKwQARcBx\n3PDhw3Nzc4lIX18/NzdXIBDIOygA6NVwHgsAFIFAIFi+fLmampqampqzszMSLACQO5zHAgAF8eDB\nA0tLS/Zg/Pjx8g4HAHo79AmF3uLMmTNnzpyRdxTQuQYOHEhE/v7+8g4EOpednZ2dnZ28owB4CVwr\nhN7izJkzSUlJ8o4COig7O1uaFHny5MmTJ0/ugnhk68yZM9nZ2fKOosdISkrC7yXoEXAeC3qRqVOn\nRkdHyzsK6Ijo6GgHBwdFffkEAsGmTZvs7e3lHUjPgAMFPQXOYwEAAADIHnIsAAAAANlDjgUAAAAg\ne8ixAAAAAGQPORYAAACA7CHHAgAAAJA95FgAoMjeeOMNLy8veUchS6mpqXv37o2KirK0tBQIBBYW\nFjU1Nfx/4+Pj582bJxAIJk2aFBUV1fXh5ebmRkRE2NvbT506Vbyc47hvv/3Wzs5uy5Ytrq6uJ0+e\nZOVCodDb2zsnJ6frQwXobBgfCwAUmZGRUd++fTuv/uzs7OHDh3de/RKuXr166NChY8eOqampzZ8/\nX11dPTk5eePGjYcOHWIrWFlZjRo1ysjIKDIycsyYMV0WGG/YsGFWVlYuLi4SW/f394+IiLh3756m\npmZpaemECROeP3++YcMGFRWVzZs3u7m57d2719jYuOsDBug8OI8FAIrs9OnTnTe1TkZGxrJlyzqp\n8uaSk5NXrFgRFhampqZGRIMHDyaiGTNmHD58WPyU1bBhw4jIyMioywKTMGLECImSrKwsf3//VatW\naWpqEpGmpqabm5uvr29xcTERaWtrb9u2beHChVVVVXIIF6DTIMcCAOiInJwcGxub58+fd83mRCKR\nk5PTypUrtbW1xcujoqL09PTc3d3T09NZiaqqKhGxPKybiIyMFAqFc+bM4Utmz55dU1Nz5MgRtmhp\naWliYqJgV3UBkGMBgGJqamqKjo52dnaeOXMmx3Hnzp1zd3c3MDAoLS11dnbW1tYeN27cnTt3OI5L\nSkry9PQcOXJkfn7+4sWLtbS0xo0bFxMTQ0Th4eECgUAgEBBRRUVFUFAQv3js2LHk5OT8/PzVq1ez\nLSYkJBgYGCQmJnbG7pw/f/7evXvvvvuuRLm+vn50dHR1dbWjo2NDQ0PzJ5aXl3t5efn4+Hh4eMyd\nO9fDw6O0tLSNA8KeVVtbu2vXLhcXl0mTJllZWT18+PBVgr9+/ToRGRgY8CXsAusff/zBl8ybN+/w\n4cNPnjx5lQ0BdC8cQO9gZ2dnZ2cn7yigg9i1sPY+Kysri4jGjBkjEomys7MHDBhARAEBAZmZmSdO\nnCCiKVOmCIXC2NhY1mdr7dq1iYmJkZGRAwcOJKLr169zHMc6CfF1ii+yyvl/nT17tl+/fufPn29v\nnEQUFRXV9jqOjo5E1NDQIPFE9mDfvn1E5OnpKVFeUVFhamq6bds2tlhQUGBqampkZFRSUtLaAWFr\nurq6Pnr0iD22trbW0dEpLy9v1x6JHxlLS0siqqmp4Uuqq6uJ6M033+RL7t69S0SBgYEvrRyfZegp\nkGNBb4F2uUfrWI4lEonEv+xHjx7NVyISiXR0dNTU1NiiqakpEVVVVbHF4OBgInJwcOA4jvXd5usU\nX5TIJDiOa2xsbG+QnHQ5lqGhobq6evMn8ruzZMkSIoqLixMv9/X1JaJnz57xT/nuu++IyMvLi2v9\ngNy6dav5D/LY2Nh27ZH4kZkxYwYR1dbW8iXsXsiJEyfyJbm5uUQ0f/78l1aOzzL0FLhWCAAKi13U\na3FRIBBoamryF9eUlJSIiJ3XIaKFCxcSUVpaWnu3qKLSWTdr5+fnsw7jLRIIBBEREWZmZs7Ozs+e\nPePLb9y4QUSDBg3iS2bOnElEN2/epNYPyO3bt83NzSW+LWxsbDocvJmZGRGVlZXxJaWlpUQ0dOhQ\nvkRDQ4OICgoKOrwVgO4GORYAgCT23d+VgzK8lLKyclNTUxsrDBo0KCYmpra29qOPPuILWe6YmZnJ\nl+jq6hKRurp6G1UVFxdnZGSwy3m8trfeNgsLCyIST/7y8vKIaPr06XwJS/g4juvwVgC6G+RYAACS\n2JgCVlZW9OK7v76+nohEIlF5eTmJpQJCoVD8iRKLMqSvry9+HoheJD3iqY+5uXlERMTVq1f5EnbW\nKi4uji/Jzs6mF7vWGjMzM9bnnS9JTk4+cOBAh4Nfvny5urr6lStX+JKEhARVVVXxkS/YmS19ff0O\nbwWgu0GOBQAKq7KykogqKirYYl1dHYmlR+y/jY2N/Pp8hnT58uWJEyeuWrWKXlznCggISEtLCw0N\nZcnWpUuXmpqaTExM8vLynj59yp514cIFDQ2Nixcvdsa+zJo1q7KyksXMFBYWUrOLa/b29hs3buQX\nvb29LSwswsLC2HkjIjp48OC0adPWrl1LrR+Q999/38jIyN/f38XFJTIy0s/Pb+PGjStXriSivXv3\nmpubnzp1qo1QWV8r8eRPS0vrs88+Cw8P51+RQ4cO+fn5iZ8pLCoqov8+swXQ0yHHAgDFVF1dHRgY\nSER5eXn79u376quv2G2GO3bsKC8vDwkJYZeutm7dWltby54SEhJSVFRUWFj47NmzxMRENtDUrl27\npkyZsm/fvjVr1ixYsMDCwsLJyamsrEwoFNrZ2Q0ePPj27dvs6X369Bk8eHCfPn06Y3dWrFhBRElJ\nSWzxn//8p4uLCxG5u7tfu3ZNfM3du3e/9dZb7HH//v2TkpKWLVvm7Ozs6enp7e09ZMgQdg7p4MGD\nrR0QjuMSEhJsbW3/9a9/eXp6FhYWRkZGsiFP09PTHz9+/Pe//721OK9cucKSvMzMzN27d9+/f5+V\ne3t7b968+dNPP92yZYuLi4u3t/fWrVvFn3jjxg0lJSUHBwfZHC+AbkCAi9/QS9jb2xNRdHS0vAOB\njoiOjmZ3+XVS/WZmZikpKfJqDwUCQVRUFHuLtuG9994bPXp0SEhI10TVmtTU1OXLl//666+yrdbW\n1lZPT+/w4cMvXROfZegpcB4LAKBnOHr0aFxcXH5+vhxjqK6u3r9/Pz8+u6wkJSWlpqYGBQXJtloA\n+UKOBQBA7B66bj5fnq6ubkxMzKZNmyTu+OtK6enpgYGB48ePl2Gdubm5O3bsiI+PZ5cjARQGciwA\n+SgoKIiOjt6xY4e8A3lV7D47aUjcFtdNVFVV+fr65uTkENH69ev5Dk/d02uvvbZjx46DBw/KK4Dx\n48fLNhNqbGw8ceLEyZMnu9VIGQAygRwLQA4ePXr05ZdfOjg4sAlMZO7KlSt2dnZsZr1Vq1axgShl\nSygU7ty5c/r06RJTFDdXV1e3Y8eOqVOnvnRNuRg4cCA/f0tERMTUqVPlHdFLGBsbe3t7yzsKmVFV\nVfXx8cEZLFBIyLF+ScOJAAAOrklEQVQA5GDs2LGd2vXknXfeYVOmjBgxIjw8nL/LTIZUVFQ2bNiQ\nnJz80qEp+/bt6+HhkZKSwma2AQDoJZBjAcgHm4S48/Tv35+I+vXr13mb6Nevn46OjmzXBABQGMix\nAAAAAGQPORbAf2loaNi+fbubm5uvr++qVat27tw5duzYtp9y5swZLS0tgUDg5+fHSr7++mslJaVD\nhw4RUWpq6pIlSzZv3rx8+fIZM2Y8ePCgeQ3h4eGs7xQRVVRUBAUF8YtExGY1cXFxmTRpkpWV1cOH\nD1l5QkKCgYFBYmJix/a0xcCqq6u///77pUuXTps2LSkpacKECYaGhtevX09JSVm0aNGQIUPMzMzu\n3LkjUVVaWpqtra2mpubkyZP5+VJqamo8PDzc3d39/Pw+++wz8VvhpDkmAAA9HgfQO9jZ2dnZ2bW9\njlAonDlzpoODQ1NTE8dxVVVVAwcOlOZjsn//fiL68ccf2WJWVtbSpUvZ41GjRhkbG3Mc19DQoK6u\nbmFhwT+LiMaMGcMeGxsbi29IfNHV1fXRo0fssbW1tY6OTnl5OcdxZ8+e7dev3/nz51uLSrz+5loM\nrKmpKS0tjYgGDx584cKFP//8k4gMDQ13795dVlZ29+5dIpo1axZfyZgxY4how4YNP/300z/+8Y/+\n/fsrKSn98ccfjY2NU6ZMcXV1FYlEHMf99ddfysrK/B61cUxaExUVpcDtFRFFRUXJO4oeQ5rPMkB3\noLBtFoAEadrlb775hoju37/Pl4wePVqar/b6+vrhw4fb2tqyRT8/v7t377LHQUFBJ0+e5DiuqanJ\n2NhYRUWFf5Z4DsSSFf5f/OKtW7ea/zSKjY1lqzU2NrYRVds5VmuBsZ7p/BOHDh3KByYSiYYMGaKu\nri4RJ8v5OI5jQ5CvWLEiLCyMiP78809+TVNTU76eNo5Ja1iOBcAgx4IeQUXenxSAbuTf//43EbFs\ngOEv2LVNTU1tw4YNXl5eT548GT58eEpKyoQJE9i/PDw8qqqqDh48WFJSUl9fz886LKXbt2+bm5uz\n80nNqah0/CPcWmASuzxo0CD+sUAg0NbWTklJkaiKv/F+0aJFGzduTE5OLi0tJSIjIyN+HSWl//RM\n6PAxUdTpU+zt7Tdt2tT9h43oJoKDg+UdAoBUkGMB/MfTp0+JqKCgQDw5kJKrq+v27dsPHDgwderU\nJUuW8OW//fabg4PD119/vWbNmsjIyPZWW1xcnJGRUV1dPWDAAL6wqamJXXrrmMLCQk1NzXv37r1K\nYC3S1dUlohEjRmRmZhJRcXGxgYFB89U6fEzs7OxkEmc39Oabbyrw3snWmTNn5B0CgFTQ5x3gP0aN\nGkVEFy5c4Evq6uqkfK66urqrq2tERERUVNQHH3zAl69YsaKxsXH+/PlExC7DcS1NPMzOHtXX17PV\n2ODpHMeZmZmxPu/8msnJyQcOHGCP23tWjNX56aefKisrSxlYu2RnZxORjY2NmZkZEcXFxbW4Wmds\nGgCgu8F5LID/WL169ZkzZ/z8/LS1tc3NzW/cuFFUVCT909evX79///4JEyaoqqryhXl5eRUVFT//\n/HNhYSGbTOa3334bOnQoG/Scz+HMzMweP34cEBCwYsWKCxcusGTr0qVLNjY2RkZG/v7+ubm5s2fP\nfvTo0W+//fbDDz8Q0YULFxwdHc+cOcOSFQnPnj0josrKSpFIxF+nKy8v9/Ly6tu3r5KSUmuBDRky\nhMSSnsbGRlYPu2jIAuZPpLHUsKSkREtLi+O44ODghQsX/r//9/8mTJgQFRXl6+traGg4a9aspKQk\nFk9GRoaRkVFrm8ZsKgCgSHAeC+A/Zs+efezYMR0dnXXr1m3dunX27NktXupqjZGR0bp16z755BPx\nwsDAwMGDB2/ZssXExGTLli0aGhqBgYH5+fk+Pj5ElJWVFRISUlpaumvXrilTpuzbt2/NmjULFiyw\nsLBwcnIqKytTUVFJSEiwtbX917/+5enpWVhYGBkZyfo/9enTZ/DgwX369GkeSUJCAgvj2bNn5ubm\n77zzzjvvvDNmzBgdHZ3Dhw9bW1u3FlhlZeWWLVuIKDMzMz4+/tKlS1lZWUS0ZcuW4uLisLAwthgU\nFMSyz/3799vY2Hz44Ydubm4bNmywsLD45z//KRAIXn/99YSEBDMzMzs7u3Hjxv3222+vv/76qlWr\n0tPTm5qaWtw0GzQVAEBhCHCKHnoJe3t7an+naTMzs5SUFHxM5C46OtrBwUFRXwiBQBAVFcXeovBS\nHfssA3Q9nMcCkIqgdY8fP5Z3dAAA0O2gPxZAq0QiUUlJCRGxYajkHQ4AAPQkOI8F0LJHjx5t2rTp\n+fPnROTj41NVVSXviABakJqaunfv3qioKEtLS4FAYGFhUVNTw/83Pj5+3rx5AoFg0qRJchnHNTc3\nNyIiwt7eXnz0L6FQ6O3tnZOT0/XxAHQl5FgALRs7dmxoaCgbqzcoKIhNqgOKio060R0qaZerV69u\n3759/fr1Dg4O165dI6Lk5OSNGzfyK1hZWYWHhxNRZGSkg4NDF4dHRMOGDbOysjpz5gwblpZRUVHZ\nvHnz+vXr09PTuz4kgC6DHAsAeruMjIxly5Z1h0raJTk5mU1bpKamRi9G258xY8bhw4fFT1kNGzaM\n/nvM/S42YsSI5oXa2trbtm1buHAhzhCDAkOOBQC9Wk5Ojo2NDbsoLN9K2kUkEjk5Oa1cuZINtMaL\niorS09Nzd3fnTxGx0dpYHtatWFpampiYeHl5yTsQgM6CHAsAFAcbZNXHx8fDw2Pu3LkeHh7sElV4\neDi7CZSIKioqgoKC+MVjx44lJyfn5+evXr2a47ikpCRPT8+RI0fm5+cvXrxYS0tr3LhxMTEx0lfC\nIklISDAwMEhMTOykPT1//vy9e/feffddiXJ9ff3o6Ojq6mpHR8eGhgYpDxHHcefOnXN3dzcwMCgt\nLXV2dtbW1h43btydO3fYs9hkAy4uLpMmTbKysnr48KFM9mLevHmHDx9+8uSJTGoD6Ha6fBZqAPmw\ns7Ozs7OTdxTQQeziV9vrVFRUmJqabtu2jS0WFBSYmpoaGRmxHMLY2Fi8BvFFIhozZgzHcUKhMDY2\ntm/fvkS0du3axMTEyMhI1hXv+vXrUlbCnD17tl+/fufPn5dm74goKipKmjV5jo6ORNTQ0CBRD3uw\nb98+IvL09JQob+0QlZSUZGdnszkxAwICMjMzT5w4QURTpkxha7q6uj569Ig9tra21tHRKS8vlz5a\niYPDu3v3LhEFBgZKXxWHzzL0HMixoLdAu9yjSZNj+fr6EtGzZ8/4ku+++46IvLy8OI4bM2aMeA3i\nixIZgKmpKRFVVVWxxeDgYCJiI6BKXwnHcWzID2l0IMcyNDRUV1dvXg97IBKJ2MTkcXFx4uVtH6LR\no0eL16Cjo6OmpsZx3K1bt5r/Po+NjZU+2tZyrNzcXCKaP3++9FVx+CxDz4FrhQCgIG7cuEFEbF5F\nZubMmUR08+bNdtXDpndkJ3WIaOHChUSUlpbW3nhUVDpxAML8/HxNTc3W/isQCCIiIszMzJydndlM\nkUzbh4hd9+Rr0NTUZFcbb9++bW5uLvHlYWNj8+p7oaGhQUQFBQWvXhVAN4QcCwAUBMuNMjMz+RJd\nXV0iUldXf5Vqhw4dSkTdbb5qZWXlpqamNlYYNGhQTExMbW3tRx99xBd27BAVFxdnZGRUV1eLF7a9\ndSmxrI7DAL+goJBjAYCCYKdk4uLi+BI2YJWVlRW9+Dqvr68nIpFIVF5eTmLf7kKhsLVqi4uLO1ZJ\nG3W+On19/bKyMvESlvSIpz7m5uYRERFXr17lS9o+RK0xMzNjfd75kuTk5AMHDrziLhARuyNBX1//\n1asC6IaQYwGAgvD29rawsAgLC8vLy2MlBw8enDZt2tq1a4nIzMyMiAICAtLS0kJDQ1medOnSpaam\nJhMTk7y8vKdPn4rXxmdIly9fnjhx4qpVq9pVyYULFzQ0NC5evNhJOztr1qzKysrKykq+pLCwkJpd\nd7O3txcfkrTtQ1RXV0diKSOrvLGx8f333zcyMvL393dxcYmMjPTz89u4cePKlSuJaO/evebm5qf+\nf3v375JaGMdx/DEIIcQpqLkhhLCxJf+CwtGGKB2ClkAwRSRoPAcS0kPgVNAQGSrq1BZKQxyjpdEc\nxNBExKFAp4IaziW6l3tvKUeP1vu1iTzHj8cffHnO83zP2dl/omp95/8679VqtYQQDoej+xMAjABq\nLADfxMTEhKqqq6urHo/H7/cHg8HJyclcLqc1iNrb21tYWIhEIltbW8vLy3Nzc2tra4+Pjy8vLy6X\ny2q13tzcfDyaoiitVqvZbNbr9cvLy24PYjabrVar2Wzu05t1u91CCFVVtYeZTGZjY0MIsbm5qTV8\nfxcOhxcXFz89RbFY7P7+XgghSdLT05OiKNpCrt3d3dfX11wu53Q6s9ms3+9vNpunp6day9NyuVws\nFgOBwL9y5vN5rcirVCrhcPj29vbjs1dXV2NjY4Y0oAcGwMSFcPwQKysrQohkMml0EPQimUxqO/sG\n8Fo2m+3u7m6Q/40mkymRSGhf0a9bWlqanZ1VFKVPqb6oVCqtr69fX1/3MNbpdE5PTx8eHnY1it8y\nRgXzWAAwko6Pj8/PzxuNhoEZOp3OwcHB0dFRD2NVVS2VSvv7+7qnAoYENRYA/EbbQDf899GbmppK\np9M+n++PHX+DVC6XZVm22+3dDnx4eJAk6eLiQrvmCHxL1FgA8Eu73d7Z2anVakIIr9f7vtppaM3P\nz0uSFIvFjApgt9t7KJKen59PTk7i8fiwdcQA9NXHFnkAMFosFossy7IsGx2kCzMzM8Fg0OgU3Rkf\nHw+FQkanAPqOeSwAAAD9UWMBAADojxoLAABAf9RYAAAA+mPNO36QarWaSqWMToFeFAoFIcQ3/vgK\nhYJ2M0R8qlqtsiERI4E+7/gptre3o9Go0SkA6MDn80UiEaNTAJ+gxgIAANAf67EAAAD0R40FAACg\nP2osAAAA/VFjAQAA6O8NZcUjI/onAwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model_loaded,'model.png',True)\n",
    "Image('model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_og = X_train.reshape((-1,28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_og = X_test.reshape((-1,28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnn_model = Sequential()\n",
    "rnn_model.add(GRU(64,input_shape=(28,28)))\n",
    "rnn_model.add(Dense(32,activation='relu'))\n",
    "rnn_model.add(Dense(num_actions, activation='softmax'))\n",
    "rnn_model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/10\n",
      "54000/54000 [==============================] - 44s 811us/step - loss: 0.6815 - acc: 0.7715 - val_loss: 0.1811 - val_acc: 0.9483\n",
      "Epoch 2/10\n",
      "54000/54000 [==============================] - 43s 805us/step - loss: 0.1820 - acc: 0.9454 - val_loss: 0.1311 - val_acc: 0.9608\n",
      "Epoch 3/10\n",
      "54000/54000 [==============================] - 44s 812us/step - loss: 0.1216 - acc: 0.9625 - val_loss: 0.0839 - val_acc: 0.9750\n",
      "Epoch 4/10\n",
      "54000/54000 [==============================] - 43s 805us/step - loss: 0.0942 - acc: 0.9711 - val_loss: 0.0717 - val_acc: 0.9783\n",
      "Epoch 5/10\n",
      "54000/54000 [==============================] - 43s 797us/step - loss: 0.0765 - acc: 0.9769 - val_loss: 0.0768 - val_acc: 0.9770\n",
      "Epoch 6/10\n",
      "54000/54000 [==============================] - 43s 803us/step - loss: 0.0647 - acc: 0.9807 - val_loss: 0.0593 - val_acc: 0.9830\n",
      "Epoch 7/10\n",
      "54000/54000 [==============================] - 44s 810us/step - loss: 0.0586 - acc: 0.9823 - val_loss: 0.0541 - val_acc: 0.9845\n",
      "Epoch 8/10\n",
      "54000/54000 [==============================] - 43s 801us/step - loss: 0.0489 - acc: 0.9849 - val_loss: 0.0544 - val_acc: 0.9850\n",
      "Epoch 9/10\n",
      "54000/54000 [==============================] - 43s 804us/step - loss: 0.0454 - acc: 0.9864 - val_loss: 0.0480 - val_acc: 0.9853\n",
      "Epoch 10/10\n",
      "54000/54000 [==============================] - 44s 812us/step - loss: 0.0401 - acc: 0.9878 - val_loss: 0.0439 - val_acc: 0.9875\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fdc84090dd8>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_model.fit(X_train_og, y_train_onehot, batch_size=64, epochs=10, validation_split=0.1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 406us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.050150478051835673, 0.98509999999999998]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_model.evaluate(X_test_og, y_test_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_dict = dict([(layer.name, layer) for layer in actor_model.layers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'actions_input': <keras.engine.topology.InputLayer at 0x7fdd90385898>,\n",
       " 'conv2d_5': <keras.layers.convolutional.Conv2D at 0x7fdd904c2f98>,\n",
       " 'conv2d_6': <keras.layers.convolutional.Conv2D at 0x7fdd904c2a20>,\n",
       " 'conv2d_7': <keras.layers.convolutional.Conv2D at 0x7fdd904a9ef0>,\n",
       " 'conv2d_8': <keras.layers.convolutional.Conv2D at 0x7fdd904320b8>,\n",
       " 'dense_3': <keras.layers.core.Dense at 0x7fdd90401198>,\n",
       " 'flatten_2': <keras.layers.core.Flatten at 0x7fdd9043dcf8>,\n",
       " 'image_inputs': <keras.engine.topology.InputLayer at 0x7fdd904c2160>,\n",
       " 'multiply_1': <keras.layers.merge.Multiply at 0x7fdd9039c160>,\n",
       " 'q_outputs': <keras.layers.core.Dense at 0x7fdd9036fb38>,\n",
       " 'q_value': <keras.layers.core.Lambda at 0x7fdd903338d0>}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = actor_model.layers[0].input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnn_model = Model(inputs=actor_model.layers[0].input,\n",
    "                  outputs = (actor_model.get_layer('conv2d_8').output))\n",
    "\n",
    "features = cnn_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_input = Input(cnn_model.output_shape[1:])\n",
    "x = Flatten()(top_input)\n",
    "x = Dense(num_actions, activation='softmax')(x)\n",
    "top_model = Model(top_input, x)\n",
    "top_model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.5570 - acc: 0.9446\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.1498 - acc: 0.9723\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1056 - acc: 0.9766\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 3s 50us/step - loss: 0.0866 - acc: 0.9787\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0759 - acc: 0.9803\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 3s 50us/step - loss: 0.0689 - acc: 0.9811\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0640 - acc: 0.9821\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 3s 50us/step - loss: 0.0604 - acc: 0.9829\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0576 - acc: 0.9835\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0553 - acc: 0.9840\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0535 - acc: 0.9843\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0519 - acc: 0.9849\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0506 - acc: 0.9852\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.0493 - acc: 0.9854\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0483 - acc: 0.9857\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0473 - acc: 0.9859\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0464 - acc: 0.9861\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0457 - acc: 0.9863\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s 50us/step - loss: 0.0450 - acc: 0.9865\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0443 - acc: 0.9869\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fda9d07f2e8>"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_model.fit(features, y_train_onehot, epochs=20, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 4s 72us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.043135224707672996, 0.98719999999999997]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_model.evaluate(features, y_train_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 126us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.057014617564086802, 0.9839]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_model.evaluate(cnn_model.predict(X_test), y_test_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "layer_name = 'conv2d_7'\n",
    "filter_index = 0  # can be any integer from 0 to 511, as there are 512 filters in that layer\n",
    "\n",
    "# build a loss function that maximizes the activation\n",
    "# of the nth filter of the layer considered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 64/64 [02:42<00:00,  2.54s/it]\n"
     ]
    }
   ],
   "source": [
    "filters_img = []\n",
    "for filter_index in tqdm(range(64)):\n",
    "    input_img_data = np.random.random((1, 28, 28,1))\n",
    "    input_img_data = (input_img_data - 0.5) * 20 + 128\n",
    "    layer_output = layer_dict[layer_name].output\n",
    "    loss = K.mean(layer_output[:, :, :, filter_index])\n",
    "\n",
    "    # compute the gradient of the input picture wrt this loss\n",
    "    grads = K.gradients(loss, input_img)[0]\n",
    "\n",
    "    # normalization trick: we normalize the gradient\n",
    "    grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
    "\n",
    "    # this function returns the loss and grads given the input picture\n",
    "    iterate = K.function([input_img], [loss, grads])\n",
    "    # run gradient ascent for 20 steps\n",
    "    for i in range(512):\n",
    "        loss_value, grads_value = iterate([input_img_data])\n",
    "        input_img_data += grads_value\n",
    "        if loss_value <= 0:\n",
    "            break\n",
    "    if loss_value>0:\n",
    "        img = deprocess_image(input_img_data[0])\n",
    "        filters_img.append((img,loss_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filters_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deprocess_image(x):\n",
    "    # normalize tensor: center on 0., ensure std is 0.1\n",
    "    x -= x.mean()\n",
    "    x /= (x.std() + 1e-5)\n",
    "    x *= 0.1\n",
    "\n",
    "    # clip to [0, 1]\n",
    "    x += 0.5\n",
    "    x = np.clip(x, 0, 1)\n",
    "\n",
    "    # convert to RGB array\n",
    "    x *= 255\n",
    "    x = np.clip(x, 0, 255).astype('uint8')\n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 5\n",
    "filters_img.sort(key=lambda x: x[1], reverse=True)\n",
    "filters_img_top = filters_img[:n * n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filters_img_top_images,_ = zip(*filters_img_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P3LaqxDCos35VdHtUxfS4YqqW3sca/YojSDUe1WcSR9ixewboGI2pIRijewFFP+HjjjsO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda20ac74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(8,8), nrows=5, ncols=5, sharey=True, sharex=True)\n",
    "for ax, img in zip(axes.flatten(), filters_img_top_images):\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "    #_, img_bin = cv2.threshold(img, 175, 255, cv2.THRESH_BINARY) \n",
    "    im = ax.imshow(img.reshape((28,28)), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
